
Orcun Goksel
Associate Professor
Dept. Information Technology, Uppsala University, Sweden
orcun.goksel@it.uu.se +46 18 471 3460
Office: Room 104147, Ångstrom building 10, Polacksbacken campus, Lägerhyddsvägen 1, 75105 Uppsala, Sweden
Post: Box 337, 75105 Uppsala, Sweden
Head of Computer-assisted Applications in Medicine (CAiM) Group
Dr. Goksel received two BSc degrees in electrical engineering (2001) and in computer science (2002) from Middle East Technical University, Ankara, Turkey. He received his MASc (2004) and PhD (2009) degrees in Electrical and Computer Engineering at the University of British Columbia, Vancouver, Canada. In 2014, he was appointed as an SNSF assistant professor at the Department of Information Technology and Electrical Engineering at ETH Zurich, Switzerland; where he founded the Computer-assisted Applications in Medicine (CAiM) group, which he has been leading. . In 2020, he joined the Department of Information Technology at Uppsala University, Sweden, as an associate professor, where he is affiliated with the Centre for Image Analysis as well as the Medtech Science and Innovation Centre. Dr. Goksel has received the 2016 ETH Spark Award (for most promising invention of the year), the 2014 CTI Swiss MedTech Award, and the 2011 WAGS Innovation in Technology Award (for best dissertation in western North America).
- Associate Professor, Department of Information Technology, Uppsala University (2020- )
- SNSF Professor, D-ITET, ETH Zurich (2014-2022)
- Senior post-doc in Medical Imaging Group at Computer Vision Lab, D-ITET, ETH Zurich (2011-2014)
- Post-doctoral Fellow at Robotics and Control Lab, ECE, UBC (2010)
- Ph.D., Electrical and Computer Eng., UBC (2005-2009)
- M.A.Sc. (Master of Applied Science), Electrical and Computer Eng., UBC (2002-2004)
- B.S., CENG, METU (1998-2002)
- B.S., EEE, METU (1997-2001)
Group CAiM
Visual summary of research interests

Publications
Journal Articles:

@unpublished{Chintada_spectral_22, author = {Bhaskara Rao Chintada and Richard Rau and Orcun Goksel}, title = {Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror}, journal = {Frontiers in Physics}, year = {2022}, volume = {10}, number = {860725}, url = {https://arxiv.org/abs/2201.01435}, doi = {10.3389/fphy.2022.860725} }

@article{Gomariz_probabilistic_22, author = {Alvaro Gomariz and Tiziano Portenier and C\'esar Nombela-Arrieta and Orcun Goksel}, title = {Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression}, journal = {Science Advances}, year = {2022}, volume = {8}, number = {5}, pages = {eabi8295}, url = {https://arxiv.org/abs/2102.11865}, doi = {10.1126/sciadv.abi8295} }

@article{Pean_computational_22, author = {Fabien P\'ean and Philippe Favre and Orcun Goksel}, title = {Computational Analysis of Subscapularis Tears and Pectoralis Major Transfers on Muscular Activity}, journal = {Clinical Biomechanics}, year = {2022}, volume = {92}, number = {105541}, url = {https://arxiv.org/abs/2012.14340}, doi = {10.1016/j.clinbiomech.2021.105541} }

@article{Pati_hierarchical_22, author = {Pushpak Pati and Guillaume Jaume and Antonio Foncubierta and Florinda Feroce and Anna Maria Anniciello and Giosu\`e Scognamiglio and Nadia Brancati and Maryse Fiche and Estelle Dubruc and Daniel Riccio and Maurizio Di Bonito and Giuseppe De Pietro and Gerardo Botti and Jean-Philippe Thiran and Maria Frucci and Orcun Goksel and Maria Gabrani}, title = {Hierarchical Graph Representations in Digital Pathology}, journal = {Medical Image Analysis}, year = {2022}, volume = {75}, number = {102264}, url = {https://arxiv.org/abs/2102.11057}, doi = {10.1016/j.media.2021.102264} }
2021

@article{Chintada_phase-aberration_21, author = {Bhaskara Rao Chintada and Richard Rau and Orcun Goksel}, title = {Phase-Aberration Correction in Shear-wave Elastography Imaging Using Local Speed-of-Sound Adaptive Beamforming}, journal = {Frontiers in Physics: Medical Physics and Imaging}, year = {2021}, volume = {9}, number = {690385}, url = {https://arxiv.org/abs/2107.02734}, doi = {10.3389/fphy.2021.690385} }

@article{Gomariz_modality_21, author = {Alvaro Gomariz and Tiziano Portenier and Patrick M. Helbling and Stephan Isringhausen and Ute Suessbier and C\'esar Nombela-Arrieta and Orcun Goksel}, title = {Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy}, journal = {Nature Machine Intelligence}, year = {2021}, volume = {3}, number = {9}, pages = {799-811}, url = {https://arxiv.org/abs/2008.12380}, doi = {10.1038/s42256-021-00379-y} }

@article{Chintada_nonlinear_21, author = {Bhaskara R. Chintada and Richard Rau and Orcun Goksel}, title = {Nonlinear Characterization of Tissue Viscoelasticity with Acoustoelastic Attenuation of Shear-Waves}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2022}, volume = {69}, number = {1}, pages = {38-53}, url = {https://arxiv.org/abs/2002.12908}, doi = {10.1109/TUFFC.2021.3105339} }

@article{Pean_influence_21, author = {Fabien P\'ean and Philippe Favre and Orcun Goksel}, title = {Influence of Rotator Cuff Integrity on Loading and Kinematics Before and After Reverse Shoulder Arthroplasty}, journal = {Journal of Biomechanics}, year = {2021}, volume = {129}, number = {110778}, url = {https://arxiv.org/abs/2012.09763}, doi = {10.1016/j.jbiomech.2021.110778} }

@article{Robin_hemodynamic_21, author = {Justine Robin and Richard Rau and Berkan Lafci and Aileen Schroeter and Michael Reiss and Xos\'e-Lu\'is De\'an-Ben and Orcun Goksel and Daniel Razansky}, title = {Hemodynamic response to sensory stimulation in mice: Comparison between functional ultrasound and optoacoustic imaging}, journal = {NeuroImage}, year = {2021}, volume = {237}, pages = {118111}, doi = {10.1016/j.neuroimage.2021.118111} }

@article{Rau_speed-of-sound_21, author = {Richard Rau and Dieter Schweizer and Valery Vishnevskiy and Orcun Goksel}, title = {Speed-of-Sound Imaging using Diverging Waves}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {2021}, volume = {16}, pages = {1201-11}, url = {https://arxiv.org/abs/1910.05935}, doi = {10.1007/s11548-021-02426-w} }

@article{Zhang_learning_21, author = {Lin Zhang and Tiziano Portenier and Orcun Goksel}, title = {Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {2021}, volume = {16}, pages = {721?730}, url = {https://arxiv.org/abs/2101.08339}, doi = {10.1007/s11548-021-02349-6} }

@article{Ruby_quantification_21, author = {Lisa Ruby and Sergio J Sanabria and Katharina Martini and Thomas Frauenfelder and Gerrolt Nico Jukema and Orcun Goksel and Marga B Rominger}, title = {Quantification of immobilization-induced changes in human calf muscle using speed-of-sound ultrasound: An observational pilot study}, journal = {Medicine}, year = {2021}, volume = {100}, number = {11}, doi = {10.1097/MD.0000000000023576} }

@article{Ozdemir_active_21, author = {Firat Ozdemir and Zixuan Peng and Philipp Fuernstahl and Christine Tanner and Orcun Goksel}, title = {Active Learning for Segmentation Based on Bayesian Sample Queries}, journal = {Knowledge-Based Systems}, year = {2021}, volume = {214}, number = {106531}, pages = {1-9}, url = {https://arxiv.org/abs/1912.10493}, doi = {10.1016/j.knosys.2020.106531} }

* Runner-up for the MICCAI Elsevier MedIA Prize
@article{Rau_frequency-dependent_21, author = {Richard Rau and Ozan Unal and Dieter Schweizer and Valery Vishnevskiy and Orcun Goksel}, title = {Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector}, journal = {Medical Image Analysis}, year = {2021}, volume = {67}, number = {101875}, pages = {1-9}, url = {https://arxiv.org/abs/2003.05658}, doi = {10.1016/j.media.2020.101875} }

@article{Pati_reducing_21, author = {Pushpak Pati and Antonio Foncubierta-Rodr\'{i}guez and Orcun Goksel and Maria Gabrani}, title = {Reducing Annotation Effort in Digital Pathology: A Co-Representation Learning Framework for Classification Tasks}, journal = {Medical Image Analysis}, year = {2021}, volume = {67}, number = {101859}, pages = {1-17}, doi = {10.1016/j.media.2020.101859} }
2020

@article{Zhang_deepN_20, author = {Lin Zhang and Valery Vishnevskiy and Orcun Goksel}, title = {Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2020}, volume = {67}, number = {12}, pages = {2553-2564}, url = {https://arxiv.org/abs/2006.10166}, doi = {10.1109/TUFFC.2020.3018424} }

@article{Bernhardt_training_20, author = {Melanie Bernhardt and Valery Vishnevskiy and Richard Rau and Orcun Goksel}, title = {Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2020}, volume = {67}, number = {12}, pages = {2584-2594}, url = {https://arxiv.org/abs/2006.14395}, doi = {10.1109/TUFFC.2020.3010186} }

@article{Pean_surface-based_20, author = {Fabien P\'ean and Orcun Goksel}, title = {Surface-based modeling of muscles: Functional simulation of the shoulder}, journal = {Medical Engineering and Physics}, year = {2020}, volume = {82}, pages = {1-12}, doi = {10.1016/j.medengphy.2020.04.010} }

@article{Kling_optical_20, author = {Sabine Kling and Hossein Khodadadi and Orcun Goksel}, title = {Optical coherence elastography based corneal strain imaging during low-amplitude intraocular pressure modulation}, journal = {Frontiers in Bioengineering and Biotechnology: Biomechanics}, year = {2020}, doi = {10.3389/fbioe.2019.00453} }
2019

@article{Otesteanu_spectral_19, author = {Corin F Otesteanu and Bhaskara R Chintada and Marga B Rominger and Sergio J Sanabria and Orcun Goksel}, title = {Spectral Quantification of Nonlinear Elasticity using Acoustoelasticity and Shear-Wave Dispersion}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2019}, volume = {66}, number = {12}, pages = {1845-1855}, url = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/362253/Otesteanu_spectral_19pre.pdf}, doi = {10.1109/TUFFC.2019.2933952} }

@article{Starkov_ultrasound_19d, author = {Rastislav Starkov and Lin Zhang and Michael Bajka and Christine Tanner and Orcun Goksel}, title = {Ultrasound Simulation with Deformable and Patient-Specific Scatterer Maps}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2019}, volume = {14}, number = {9}, pages = {1589-1599}, doi = {10.1007/s11548-019-02054-5} }

@article{Ozdemir_extending_19, author = {Firat Ozdemir and Orcun Goksel}, title = {Extending pretrained segmentation networks with additional anatomical structures}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2019}, volume = {14}, number = {7}, pages = {1187-1195}, url = {https://arxiv.org/abs/1811.04634}, doi = {10.1007/s11548-019-01984-4} }

@article{Ruby_breast_19, author = {Lisa Ruby and Sergio J. Sanabria and Katharina Martini and Konstantin J. Dedes and Denise Vorburger and Ece Oezkan and Thomas Frauenfelder and Orcun Goksel and Marga B. Rominger}, title = {Breast Cancer Assessment With Pulse-Echo Speed of Sound Ultrasound From Intrinsic Tissue Reflections: Proof-of-Concept}, journal = {Investigative Radiology}, year = {2019}, volume = {54}, number = {7}, pages = {419-427}, url = {https://www.zora.uzh.ch/id/eprint/170532/1/document.pdf}, doi = {10.1097/RLI.0000000000000553} }

@article{Sanabria_speed-of-sound_19, author = {Sergio J Sanabria and Marga B Rominger and Orcun Goksel}, title = {Speed-of-Sound Imaging Based on Reflector Delineation}, journal = {IEEE Trans Biomedical Engineering}, year = {2019}, volume = {66}, number = {7}, pages = {1949-1962}, url = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/310433/SoSwithreflector.pdf}, doi = {10.1109/TBME.2018.2881302} }

@article{Gong_weighted_19, author = {Yuanhao Gong and Orcun Goksel}, title = {Weighted Mean Curvature}, journal = {Signal Processing}, year = {2019}, volume = {164}, pages = {329-339}, url = {https://arxiv.org/abs/1903.07189}, doi = {10.1016/j.sigpro.2019.06.020} }

@article{Hess_in-vivo_19, author = {Max Hess and Alvaro Gomariz and Orcun Goksel and Collin Ewald}, title = {In-vivo quantitative image analysis of age-related morphological changes of C. elegans neurons reveals a correlation between neurite bending and novel neurite outgrowths}, journal = {eNeuro}, year = {2019}, volume = {6}, number = {4}, doi = {10.1523/ENEURO.0014-19.2019} }

@article{Ruby_breast-density_19, author = {Lisa Ruby and Sergio J Sanabria and Anika S Obrist and Katharina Martini and Serafino Forte and Orcun Goksel and Thomas Frauenfelder and Rahel A Kubik-Huch and Marga B Rominger}, title = {Breast Density Assessment in Young Women with Handheld Ultrasound Based on Speed of Sound: Influence of the Menstrual Cycle}, journal = {Medicine}, year = {2019}, volume = {98}, number = {25}, pages = {e16123}, doi = {10.1097/MD.0000000000016123} }

@article{Starkov_ultrasound_19, author = {Rastislav Starkov and Christine Tanner and Michael Bajka and Orcun Goksel}, title = {Ultrasound Simulation with Animated Anatomical Models and On-the-Fly Fusion with Real Images via Path Tracing}, journal = {Computers & Graphics}, year = {2019}, volume = {82}, pages = {44-52}, url = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/345177/StarkovR_ComputGraph2019_UltrasoundAnimated.pdf}, doi = {10.1016/j.cag.2019.05.005} }

@article{Pean_comprehensive_19, author = {Fabien Pean and Christine Tanner and Christian Gerber and Philipp Fuernstahl and Orcun Goksel}, title = {A comprehensive and volumetric musculoskeletal model for the dynamic simulation of the shoulder function}, journal = {Computer Methods in Biomechanics and Biomedical Engineering (CMBBE)}, year = {2019}, volume = {22}, number = {7}, pages = {740-751}, doi = {10.1080/10255842.2019.1588963} }

@article{Arar_high_19, author = {Nuri Murat Arar and Pushpak Pati and Aditya Kashyap and Anna Fomitcheva Khartchenko and Orcun Goksel and Govind V. Kaigala and Maria Gabrani}, title = {High-Quality Immunohistochemical Stains through Computational Assay Parameter Optimization}, journal = {IEEE Trans Biomedical Engineering}, year = {2019}, volume = {66}, number = {10}, pages = {2952-63}, doi = {10.1109/TBME.2019.2899156} }

@article{Mahrenke_comprehensive_19, author = {Torben Marhenke and Sergio J. Sanabria and Bhaskara Rao Chintada and Roman Furrer and J{\"u}rg Neuenschwander and Orcun Goksel}, title = {Fast Spatial Characterization of Acoustic Fields Generated by Medical Array Transducers Based on Single-Plane Hydrophone Measurements}, journal = {Sensors}, year = {2019}, volume = {19}, number = {4}, pages = {863}, doi = {10.3390/s19040863} }

METHODS: The dynamically dEformable Liver PHAntom (ELPHA) was designed to fulfill three main constraints: First, a reproducibly deformable anatomy is required. Second, the phantom should provide multi-modality imaging contrast for motion detection. Third, a time-resolved dosimetry system to measure temporal effects should be provided. An artificial liver with vasculature was casted from soft silicone mixtures. The silicones allow for deformation and radiographic image contrast, while added cellulose provides ultrasonic contrast. An actuator was used for compressing the liver in the inferior direction according to a prescribed respiratory motion trace. Electromagnetic (EM) transponders integrated in ELPHA help provide ground truth motion traces. They were used to quantify the motion reproducibility of the phantom and to validate motion-detection based on ultrasound imaging. A two-dimensional ultrasound probe was used to follow the position of the vessels with a template-matching algorithm. This detected vessel motion was compared to the EM transponder signal by calculating the root-mean square error (RMSE). ELPHA was then used to investigate the dose deposition of dynamic treatment deliveries. Two dosimetry systems, radio-chromic film and plastic scintillation dosimeters (PSD), were integrated in ELPHA. The PSD allow for time-resolved measurement of the delivered dose, which was compared to a time-resolved dose of the treatment planning system. Film and PSD were used to investigate dose delivery to the deforming phantom without motion compensation and with treatment-couch tracking for motion compensation.
RESULTS: ELPHA showed densities of 66 and 45 HU in the liver and the surrounding tissues. A high motion reproducibility with a submillimeter RMSE (<0.32 mm) was measured. The motion of the vasculature detected with ultrasound agreed well with the EM transponder position (RMSE <1 mm). A time-resolved dosimetry system with a 1 Hz time resolution was achieved with the PSD. The agreement of the planned and measured dose to the PSD decreased with increasing motion amplitude: A dosimetric RMSE of 1.2, 2.1 and 2.7 cGy/s was measured for motion amplitudes of 8, 16 and 24 mm, respectively. With couch tracking as motion compensation, these values decreased to 1.1, 1.4 and 1.4 cGy/s. This is closer to the static situation with 0.7 cGy/s. Film measurements showed that couch tracking was able to compensate for motion with a mean target dose within 5% of the static situation (-5% to +1%), which was higher than in the uncompensated cases (-41% to -1%).
CONCLUSIONS: ELPHA is a deformable liver phantom with high motion reproducibility. It was demonstrated to be suitable for the verification of motion-detection and motion-mitigation modalities. Based on the multi-modality image contrast, a high accuracy of ultrasound based motion detection was shown. With the time-resolved dosimetry system, ELPHA is suitable for performance assessment of real-time motion-adaptive radiotherapy, as was shown exemplary with couch tracking.
@article{Ehrbar_elpha_19, author = {Stefanie Ehrbar and Alexander J\"ohl and Michael K\"uhni and Mirko Meboldt and Ece Ozkan Elsen and Christine Tanner and Orcun Goksel and Stephan Kl\"ock and Jan Unkelbach and Matthias Guckenberger and Stephanie Tanadini-Lang}, title = {ELPHA: Dynamically deformable liver phantom for real-time motion-adaptive radiotherapy treatments}, journal = {Medical Physics}, year = {2019}, volume = {46}, number = {2}, pages = {839-850}, doi = {10.1002/mp.13359} }

METHODS: Both calf muscles of 11 healthy, young females (mean age 29 years), and 10 elderly females (mean age 82 years) were prospectively examined with a standard ultrasound machine. A flat plexiglas reflector, on the opposite side of the probe with the calf in between, was used as timing reference for SoS (m/s) and ΔSoS (variation of SoS, m/s). Handgrip strength (kPA), Tegner activity scores, and 5-point comfort score (1 = comfortable to 5 = never again) were also assessed. Ultrasound parameters (muscle/adipose thickness, echo intensity) were measured for comparison.
RESULTS: Both calves were assessed in less than two minutes. All measurements were successful. The elderly females showed significantly lower SoS (1516 m/s, SD17) compared to the young adults (1545 m/s, SD10; p<0.01). The ΔSoS of elderly females was significantly higher (12.2 m/s, SD3.6) than for young females (6.4 m/s, SD1.5; p<0.01). Significant correlations of SoS with hand grip strength (r=0.644) and Tegner activity score (rs=0.709) were found, of similar magnitude as the correlation of hand grip strength with Tegner activity score (rs=0.794). The average comfort score of the elderly was 1.1 and for the young adults 1.4. SoS senior/young classification (AUC=0.936) was superior to conventional US parameters.
CONCLUSIONS: There were significant differences of SoS and ΔSoS between young and elderly females. Measurements were fast and well tolerated. The novel technique shows potential for sarcopenia quantification using a standard ultrasound machine.
@article{Sanabria_speed_19, author = {Sergio J Sanabria and Katharina Martini and Gregor Freyst\"{a}tter and Lisa Ruby and Orcun Goksel and Thomas Frauenfelder and Marga B Rominger}, title = {Speed of sound ultrasound: a pilot study on a novel technique to identify sarcopenia in seniors}, journal = {European Radiology}, year = {2019}, volume = {29}, number = {1}, pages = {3-12}, doi = {10.1007/s00330-018-5742-2} }
2018

@article{Otesteanu_robust_18, author = {Corin F Otesteanu and Sergio J Sanabria and Orcun Goksel}, title = {Robust Reconstruction of Elasticity Using Ultrasound Imaging and Multi-frequency Excitations}, journal = {IEEE Trans Medical Imaging}, year = {2018}, volume = {37}, number = {11}, pages = {2502-2513}, doi = {10.1109/TMI.2018.2837390} }

@article{Sanabria_spatial_18, author = {Sergio J Sanabria and Ece Ozkan and Marga B Rominger and Orcun Goksel}, title = {Spatial Domain Reconstruction for Imaging Speed-of-Sound with Pulse-Echo Ultrasound: Simulation and In-Vivo Study}, journal = {Physics in Medicine and Biology}, year = {2018}, volume = {63}, pages = {215015}, doi = {10.1088/1361-6560/aae2fb} }

Methods: We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins.
Results: Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%.
Conclusions: Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.
@article{DeLuca_evaluation_18, author = {Valeria De Luca and Jyotirmoy Banerjee and Andre Hallack and Satoshi Kondo and Maxim Makhinya and Daniel Nouri and Lucas Royer and Amalia Cifor and Guillaume Dardenne and Orcun Goksel and Mark J. Gooding and Camiel Klink and Alexandre Krupa and Anthony Le Bras and Maud Marchal and Adriaan Moelker and Wiro J. Niessen and Bartlomiej W. Papiez and Alex Rothberg and Julia A. Schnabel and Theo van Walsum and Erwin Vast and Emma Harris and Muyinatu A. Lediju Bell and Christine Tanner}, title = {Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins}, journal = {Medical Physics}, year = {2018}, volume = {45}, number = {11}, pages = {4986-5003}, doi = {10.1002/mp.13152} }

To assess feasibility and diagnostic accuracy of a novel hand-held ultrasound (US) method for breast density assessment that measures the speed of sound (SoS), in comparison to the ACR mammographic (MG) categories.
Methods
ACR-MG density (a=fatty to d=extremely dense) and SoS-US were assessed in the retromamillary, inner and outer segments of 106 women by two radiographers. A conventional US system was used for SoS-US. A reflector served as timing reference for US signals transmitted through the breasts. Four blinded readers assessed average SoS (m/s), ΔSoS (segment-variation SoS; m/s) and the ACR-MG density. The highest SoS and ΔSoS values of the three segments were used for MG-ACR whole breast comparison.
Results
SoS-US breasts were examined in <2 min. Mean SoS values of densities a-d were 1,421 m/s (SD 14), 1,432 m/s (SD 17), 1,448 m/s (SD 20) and 1,500 m/s (SD 31), with significant differences between all groups (p<0.001). The SoS-US comfort scores and inter-reader agreement were significantly better than those for MG (1.05 vs. 2.05 and 0.982 vs. 0.774; respectively). A strong segment correlation between SoS and ACR-MG breast density was evident (rs=0.622, p=<0.001) and increased for full breast classification (rs=0.746, p=<0.001). SoS-US allowed diagnosis of dense breasts (ACR c and d) with sensitivity 86.2%, specificity 85.2% and AUC 0.887.
Conclusions
Using hand-held SoS-US, radiographers measured breast density without discomfort, readers evaluated measurements with high inter-reader agreement, and SoS-US correlated significantly with ACR-MG breast-density categories.
@article{Sanabria_breast-density_18, author = {Sergio J Sanabria and Orcun Goksel and Katharina Martini and Serafino Forte and Thomas Frauenfelder and Rahel A Kubik-Huch and Marga B Rominger}, title = {Breast-Density Assessment with Handheld Ultrasound: A Novel Biomarker to Assess Breast Cancer Risk and to Tailor Screening?}, journal = {European Radiology}, year = {2018}, volume = {28}, number = {8}, pages = {3165-3175}, doi = {10.1007/s00330-017-5287-9} }

@article{Samei_real-time_18, author = {Golnoosh Samei and Orcun Goksel and Julio Lobo and Omid Mohareri and Peter Black and Robert Rohling and Septimiu Salcudean}, title = {Real-time FEM-based Registration of 3D to 2.5D Transrectal Ultrasound Images}, journal = {IEEE Trans Medical Imaging}, year = {2018}, volume = {37}, number = {8}, pages = {1877-86}, doi = {10.1109/TMI.2018.2810778} }

@article{Gomariz_quantitative_18, author = {Alvaro Gomariz and Stephan Isringhausen and Patrick Helbling and Ute Suessbier and Anton Becker and Andreas Boss and Takashi Nagasawa and Gr\'egory Paul and Orcun Goksel and G\'abor Sz\'ekely and Szymon Stoma and Simon F. N\/orrelykke and Markus G. Manz and C\'esar Nombela-Arrieta}, title = {Quantitative spatial analysis of hematopoiesis-regulating stromal cells in the bone marrow microenvironment by multiscale 3D microscopy}, journal = {Nature Communications}, year = {2018}, volume = {9}, number = {2532}, doi = {10.1038/s41467-018-04770-z} }

Methods: We propose a novel inverse problem formulation and elasticity reconstruction method, in which both the elasticity parameters and the model displacements are estimated as independent parameters of an unconstrained optimization problem. Total variation regularization of spatial elasticity distribution is introduced in this formulation, providing robustness to noise.
Results: Our approach was compared to state of the art direct and iterative harmonic elastography techniques. We employed numerical simulation studies using various noise and inclusion contrasts, given multiple excitation frequencies. Compared to alternatives, our method leads to a decrease in RMSE of up to 50% and an increase in CNR of up to 11 dB in numerical simulations. The methods were also compared on an ex vivo bovine liver sample that was locally subjected to ablation, for which improved lesion delineation was obtained with our proposed method. Our method takes ≈4s for 20x20 reconstruction grid.
Conclusions: We present a novel FEM problem formulation that improves reconstruction accuracy and inclusion delineation compared to currently available techniques.
@article{Otesteanu_fem_18, author = {Corin F Otesteanu and Valeriy Vishnevskiy and Orcun Goksel}, title = {FEM Based Elasticity Reconstruction Using Ultrasound For Imaging Tissue Ablation}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2018}, volume = {13}, number = {6}, pages = {885-894}, doi = {10.1007/s11548-018-1714-x} }

Methods: A partial bone surface is extracted from US using phase symmetry and a factor graph-based approach. This is registered to the detailed 3D bone model, conventionally generated for preoperative planning, based on a proposed multi-initialization and surface-based scheme robust to partial surfaces.
Results: 36 forearm US volumes acquired using a tracked US probe were independently registered to a 3D model of the radius, manually extracted from MRI. Given intraoperative time restrictions, a computationally efficient algorithm was determined based on a comparison of different approaches. For all 36 registrations, a mean (+- SD) point-to-point surface distance of 0.57(+-0.08)mm was obtained from manual gold standard US bone annotations (not used during the registration) to the 3D bone model.
Conclusions: A registration framework based on the bone surface extraction from 3D freehand US and a subsequent fast, automatic surface alignment robust to single-sided view and large false-positive rates from US was shown to achieve registration accuracy feasible for practical orthopedic scenarios and a qualitative outcome indicating good visual image alignment.
@article{Ciganovic_registration_18, author = {Matija Ciganovic and Firat Ozdemir and Fabien Pean and Philipp Fuernstahl and Christine Tanner and Orcun Goksel}, title = {Registration of 3D Freehand Ultrasound to a Bone Model for Orthopaedic Procedures of the Forearm}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2018}, volume = {13}, number = {6}, pages = {827-836}, doi = {10.1007/s11548-018-1756-0} }

@article{Mattausch_image-based_18, author = {Oliver Mattausch and Orcun Goksel}, title = {Image-based Reconstruction of Tissue Scatterers using Beam Steering for Ultrasound Simulation}, journal = {IEEE Trans Medical Imaging}, year = {2018}, volume = {37}, number = {3}, pages = {767-780}, doi = {10.1109/TMI.2017.2770118} }

@article{Ozkan_inverse_18, author = {Ece Ozkan and Valeriy Vishnevsky and Orcun Goksel}, title = {Inverse Problem of Ultrasound Beamforming with Sparsity Constraints and Regularization}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2018}, volume = {65}, number = {3}, pages = {356-365}, doi = {10.1109/TUFFC.2017.2757880} }

@article{Mattausch_realistic_18, author = {Oliver Mattausch and Maxim Makhinya and Orcun Goksel}, title = {Realistic Ultrasound Simulation of Complex Surface Models Using Interactive Monte-Carlo Path Tracing}, journal = {Computer Graphics Forum}, year = {2018}, volume = {37}, number = {1}, pages = {202-213}, doi = {10.1111/cgf.13260} }

@article{Ozkan_compliance_18, author = {Ece Ozkan and Orcun Goksel}, title = {Compliance Boundary Conditions for Patient-Specific Deformation Simulation Using the Finite Element Method}, journal = {Biomedical Physics & Engineering Express}, year = {2018}, volume = {4}, number = {2}, pages = {025003}, doi = {10.1088/2057-1976/aa918d} }
2017

@article{Lundin_automatic_17, author = {Elin L. Lundin and Martin Stauber and Panagiota Papageorgiou and Martin Ehrbar and Chafik Ghayord and Franz E. Weber and Christine Tanner and Orcun Goksel}, title = {Automatic Registration of 2D Histological Sections to 3D microCT Volumes: Trabecular Bone}, journal = {Bone}, year = {2017}, volume = {105}, pages = {173-183}, doi = {10.1016/j.bone.2017.08.021} }

@article{Tanner_consistent_17, author = {Christine Tanner and Barbara Flach and C\'{e}line Eggenberger and Oliver Mattausch and Michael Bajka and Orcun Goksel}, title = {Consistent Reconstruction of 4D Fetal Heart Ultrasound Images to Cope with Fetal Motion}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2017}, volume = {12}, number = {8}, pages = {1307-17}, doi = {10.1007/s11548-017-1624-3} }

@article{Ozdemir_interactive_17, author = {Firat Ozdemir and Neerav Karani and Philipp Fuernstahl and Orcun Goksel}, title = {Interactive Segmentation in MRI for Orthopedic Surgery Planning: Bone Tissue}, journal = {Int J Comp. Assisted Radiol. Surgery}, year = {2017}, volume = {12}, number = {6}, pages = {1031-9}, doi = {10.1007/s11548-017-1570-0} }

@article{Ozkan_robust_17, author = {Ece Ozkan and Christine Tanner and Matej Kastelic and Oliver Mattausch and Maxim Makhinya and Orcun Goksel}, title = {Robust Motion Tracking in Liver from 2D Ultrasound Images Using Supporters}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2017}, volume = {12}, number = {6}, pages = {941-950}, doi = {10.1007/s11548-017-1559-8} }

Regularization can help to avoid both physically implausible displacement fields and local minima during optimization.
Tikhonov regularization (squared L2-norm) is unable to correctly redrepresent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration.
In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces.
We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM).
The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images.
It provided accurate registration results for the whole volume.
A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces.
Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases.
The average target registration error (TRE) of our method is superior (10% to 40%) to other techniques in the literature.
It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95mm for DIR 4DCT, 0.96mm for DIR COPDgene, 0.91mm for POPI databases.
@article{Vishnevskiy_isotropic_17, author = {Valeriy Vishnevskiy and Tobias Gass and Gabor Szekely and Christine Tanner and Orcun Goksel}, title = {Isotropic Total Variation Regularization of Displacements in Parametric Image Registration}, journal = {IEEE Trans Medical Imaging}, year = {2017}, volume = {36}, number = {2}, pages = {385-395}, doi = {10.1109/tmi.2016.2610583} }
2016 and Earlier

This report gives an overview on topics discussed at the 4D workshops in 2014 and 2015. It summarizes recent findings, developments and challenges in the field and discusses the relevant literature of recent years. The report is structured in three parts pointing out developments in the context of understanding moving geometries, of treating moving targets, and of 4D quality assurance (QA) and 4D dosimetry.
The community represented at the 4D workshops agrees that research in the context of treating moving targets with scanned ion beams faces a crucial phase of clinical translation. In the coming years it will be important to define standards for motion monitoring, to establish 4D treatment planning guidelines, and to develop 4D QA tools. These basic requirements for the clinical application of scanned ion beams to moving targets could, e.g., be determined by a dedicated ESTRO task group.
Besides reviewing recent research results and pointing out urgent needs when treating moving targets with scanned ion beams, the report also gives an outlook on the upcoming 4D workshop organized at the University Medical Center Groningen (UMCG) in the Netherlands at the end of 2016.
@article{Knopf_required_16, author = {Antje-Christin Knopf and Kristin St\"utzer and Christian Richter and Antoni Rucinsk and Joakim da Silva and Justin Phillips and Martijn Engelsman and Shinichi Shimizu and Rene Werner and Annika Jakobi and Orcun Goksel and Ye Zhang and Tuathan Oshea and Martin Fast and Rosalind Perrin and Christoph Bert and EriK Korevaar and Jamie McClelland}, title = {Required transition from research to clinical application: report on the 4D treatment planning workshops 2014 and 2015}, journal = {Physica Medica: European J Medical Physics}, year = {2016}, volume = {32}, number = {7}, pages = {874-82}, doi = {10.1016/j.ejmp.2016.05.064} }

@article{Jimenez_cloud-based_16, author = {Oscar Alfonso {Jim\'enez-del-Toro} and Henning M\"uller and Markus Krenn and Katharina Gruenberg and Abdel Aziz Taha and Marianne Winterstein and Ivan Eggel and Antonio Foncubierta-Rodr\'iguez and Orcun Goksel and Andr\'as Jakab and Georgios Kontokotsios and Georg Langs and Bjoern Menze and Tom\`as Salas Fernandez and Roger Schaer and Anna Walley and Marc-Andr/e Weber and Yashin Dicente Cid and Tobias Gass and Mattias Heinrich and Fucang Jia and Fredrik Kahl and Razmig Kechichian and Dominic Mai and Assaf B. Spanier and Graham Vincent and Chunliang Wang and Daniel Wyeth and Allan Hanbury}, title = {Cloud-based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks}, journal = {IEEE Trans Medical Imaging}, year = {2016}, volume = {35}, number = {11}, pages = {2459-75}, doi = {10.1109/tmi.2016.2578680} }

METHODS: The 3D models of 140 paired humeri (70 cadavers) were extracted from computed tomographic data. Geometric characteristics quantifying the humeral anatomy in 3D were determined in a semiautomatic fashion using the developed computer algorithms. The results between the sides were compared for evaluating bilateral differences.
RESULTS: The mean bilateral difference of the humeral retrotorsion angle was 6.7^circ (standard deviation [SD], 5.7^circ; range, -15.1^circ to 24.0^circ; P = .063); the mean side difference of the humeral head inclination angle was 2.3^circ (SD, 1.8^circ; range, -5.1^circ to 8.4^circ; P = .12). The side difference in humeral length (mean, 2.9 mm; SD, 2.5 mm; range, -8.7 mm to 10.1 mm; P = .04) was significant. The mean side difference in the head sphere radius was 0.5 mm (SD, 0.6 mm; range, -3.2 mm to 2.2 mm; P = .76), and the mean side difference in humeral head height was 0.8 mm (SD, 0.6 mm; range, -2.4 mm to 2.4 mm; P = .44).
CONCLUSIONS: The contralateral anatomy may serve as a reliable reconstruction template for humeral length, humeral head radius, and humeral head height if it is analyzed with 3D algorithms. In contrast, determining humeral head retrotorsion and humeral head inclination from the contralateral anatomy may be more prone to error.
@article{Vlachopoulos_computer_16, author = {Lazaros Vlachopoulos and Celestine D\"unner and Tobias Gass and Matthias Graf and Orcun Goksel and Christian Gerber and G\'abor Sz\'ekely and Philipp F\"urnstahl}, title = {Computer algorithms for three-dimensional measurement of humeral anatomy: analysis of 140 paired humeri}, journal = {J Shoulder and Elbow Surgery}, year = {2016}, volume = {25}, number = {2}, pages = {e38-e48}, doi = {10.1016/j.jse.2015.07.027} }

* Having been selected as the best project of the year, this work received 2014 CTI MedTech Award at the funding agency's annual event.
@article{Crimi_automatic_16, author = {Alessandro Crimi and Maxim Makhinya and Ulrich Baumann and Christoph Thalhammer and Gabor Szekely and Orcun Goksel}, title = {Automatic Measurement of Venous Pressure Using B-Mode Ultrasound}, journal = {IEEE Trans Biomedical Engineering}, year = {2016}, volume = {63}, number = {2}, pages = {288-299}, doi = {10.1109/TBME.2015.2455953} }

@article{Khallaghi_biomechanically_15, author = {Siavash Khallaghi and C. Antonio S\'{a}nchez and Joy Sun and Farhad Imani and Amir Khojaste Galesh Khale and Orcun Goksel and Abtin Rasoulian and Cesare Romagnoli and Hamidreza Abdi and Silvia Chang and Parvin Mousavi and Aaron Fenster and Aaron Ward and Sidney Fels and Purang Abolmaesumi}, title = {Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions}, journal = {IEEE Trans Med Imag}, year = {2015}, volume = {34}, number = {11}, pages = {2404-14}, doi = {10.1109/TMI.2015.2440253} }

@article{Gass_consistency-based_15, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Consistency-Based Rectification of Non-Rigid Registrations}, journal = {SPIE J Medical Imaging}, year = {2015}, volume = {2}, number = {1}, pages = {014005}, doi = {10.1117/1.JMI.2.1.014005} }

* Received 3rd place (of 100 papers) in IPCAI/PHILIPS Best Paper Award at Information Processing in Computer-Assisted Interventions Conference, Barcelona, Spain, June 2015
@article{Baki_thermal_15, author = {Peter Baki and Sergio J Sanabria and Gabor Kosa and Gabor Szekely and Orcun Goksel}, title = {Thermal Expansion Imaging for Monitoring Lesion Depth using M-Mode Ultrasound during Cardiac RF Ablation: In-vitro Study}, journal = {Int J Computer Assisted Radiology and Surgery}, year = {2015}, volume = {10}, number = {6}, pages = {681-693}, doi = {10.1007/s11548-015-1203-4} }

@article{Gass_simultaneous_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Simultaneous Segmentation and Multi-Resolution Nonrigid Atlas Registration}, journal = {IEEE Trans Image Processing}, year = {2014}, volume = {23}, number = {7}, pages = {2931-43}, doi = {10.1109/TIP.2014.2322447} }

@article{Goksel_prostate_13, author = {Orcun Goksel and Kirill Sapchuk and William James Morris and Septimiu E. Salcudean}, title = {Prostate Brachytherapy Training with Simulated Ultrasound and Fluoroscopy Images}, journal = {IEEE Trans Biomedical Engineering}, year = {2013}, volume = {60}, number = {4}, pages = {1002-12}, doi = {10.1109/TBME.2012.2222642} }

@article{Goksel_mesh_13, author = {Orcun Goksel and Hani Eskandari and Septimiu E. Salcudean}, title = {Mesh Adaptation for Improving Elasticity Reconstruction using the FEM Inverse Problem}, journal = {IEEE Trans Medical Imaging}, year = {2013}, volume = {32}, number = {2}, pages = {408-418}, doi = {10.1109/TMI.2012.2228664} }

for PCA based statistical models.Statistical models are used to describe
the variability of an object within a population, learned from a
set of training samples. Originally developed to model shapes, statistical
models are now increasingly used to model the variation in different
kind of data, such as for example images, volumetric meshes or deformation
fields. Statismo has been developed with the following main goals
in mind: 1) To provide generic tools for learning different kinds
of PCA based statistical models, such as shape, appearance or deformations
models. 2) To make the exchange of such models easier among different
research groups and to improve the reproducibility of the models.
3) To allow for easy integration of new methods for model building
into the framework. To achieve the first goal, we have abstracted
all the aspects that are specific to a given model and data representation,
into a user defined class. This does not only make it possible to
use Statismo to create different kinds of PCA models, but also allows
Statismo to be used with any toolkit and data format. To facilitate
data exchange, Statismo defines a storage format based on HDF5, which
includes all the information necessary to use the model, as well
as meta-data about the model creation, which helps to make model
building reproducible. The last goal is achieved by providing a clear
separation between data management, model building and model representation.
In addition to the standard method for building PCA models, Statismo
already includes two recently proposed algorithms for building conditional
models, as well as convenience tools for facilitating cross-validation
studies. Although Statismo has been designed to be independent of
a particular toolkit, special efforts have been made to make it directly
useful for VTK and ITK. Besides supporting model building for most
data representations used by VTK and ITK, it also provides an ITK
transform class, which allows for the integration of Statismo with
the ITK registration framework. This leverages the efforts from the
ITK project to readily access powerful methods for model fitting.
@article{Luethi_statismo_12, author = {Marcel L\"uthi and Remi Blanc and Thomas Albrecht and Tobias Gass and Orcun Goksel and Philippe B\"uchler and Michael Kistler and Habib Bousleiman and Mauricio Reyes and Philippe Cattin and Thomas Vetter}, title = {Statismo - A framework for PCA based statistical models}, journal = {Insight Journal}, year = {2012}, url = {http://hdl.handle.net/10380/3371} }

@article{Eskandari_dilatation_12, author = {Hani Eskandari and Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {Dilatation parameterization for two dimensional modeling of nearly incompressible isotropic materials}, journal = {Physics in Medicine and Biology}, year = {2012}, volume = {57}, number = {12}, pages = {4055-73}, doi = {10.1088/0031-9155/57/12/4055} }

@article{Abeysekera_analysis_12, author = {Jeffrey M. Abeysekera and Reza Zahiri-Azar and Orcun Goksel and Robert Rohling and Septimiu E. Salcudean}, title = {Analysis of 2-D Motion Tracking in Ultrasound with Dual Transducers}, journal = {Ultrasonics}, year = {2012}, volume = {52}, number = {1}, pages = {156-168}, doi = {10.1016/j.ultras.2011.07.011} }

@article{Azar_comparison_11, author = {Reza Zahiri Azar and Orcun Goksel and Septimiu E. Salcudean}, title = {Comparison Between 2-D Cross Correlation With 2-D Sub-Sampling and 2-D Tracking Using Beam Steering}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2011}, volume = {58}, number = {8}, pages = {1534-7}, doi = {10.1109/TUFFC.2011.1978} }

@article{Eskandari_bandpass_11, author = {Hani Eskandari and Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {Bandpass Sampling of High Frequency Tissue Motion}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2011}, volume = {58}, number = {7}, pages = {1332-43}, doi = {10.1109/TUFFC.2011.1953} }

* Featured on the issue cover page
@article{Goksel_haptic_11, author = {Orcun Goksel and Kirill Sapchuk and Septimiu E. Salcudean}, title = {Haptic Simulator for Prostate Brachytherapy with Simulated Needle and Probe Interaction}, journal = {IEEE Trans Haptics}, year = {2011}, volume = {4}, number = {3}, pages = {188-198}, doi = {10.1109/TOH.2011.34} }

@article{Goksel_image-based_11, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {Image-Based Variational Meshing}, journal = {IEEE Trans Medical Imaging}, year = {2011}, volume = {30}, number = {1}, pages = {11-21}, doi = {10.1109/TMI.2010.2055884} }

@article{Zahiri-Azar_sub-sample_10, author = {Reza Zahiri-Azar and Orcun Goksel and Septimiu E. Salcudean}, title = {Sub-sample Displacement Estimation from Digitized Ultrasound RF Signals Using Multi-Dimensional Polynomial Fitting of the Cross-correlation Function}, journal = {IEEE Trans Ultrasonics, Ferroelectrics, and Frequency Control}, year = {2010}, volume = {57}, number = {11}, pages = {2403-20}, doi = {10.1109/TUFFC.2010.1708} }

@article{Goksel_b-mode_09, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {B-Mode Ultrasound Image Simulation in Deformable 3-D Medium}, journal = {IEEE Trans Medical Imaging}, year = {2009}, volume = {28}, number = {11}, pages = {1657-69}, doi = {10.1109/TMI.2009.2016561} }

@article{Goksel_modeling_09, author = {Orcun Goksel and Ehsan Dehghan and Septimiu E. Salcudean}, title = {Modeling and Simulation of Flexible Needles}, journal = {Medical Engineering and Physics}, year = {2009}, volume = {31}, number = {9}, pages = {1069-78}, doi = {10.1016/j.medengphy.2009.07.007} }

@article{Goksel_3d_06, author = {Orcun Goksel and Septimiu E. Salcudean and Simon P. DiMaio}, title = {3D Simulation of Needle-Tissue Interaction with Application to Prostate Brachytherapy}, journal = {Computer Aided Surgery}, year = {2006}, volume = {11}, number = {6}, pages = {279-288}, doi = {10.3109/10929080601089997} }

@article{French_computing_05, author = {Danny G. French and James Morris and Mira Keyes and Orcun Goksel and Septimiu E. Salcudean}, title = {Computing Intraoperative Dosimetry for Prostate Brachytherapy Using TRUS and Fluoroscopy}, journal = {Academic Radiology}, year = {2005}, volume = {12}, number = {10}, pages = {1262-72}, doi = {10.1016/j.acra.2005.05.026} }
Book Chapters:
[2022] Pushpak Pati, Guillaume Jaume, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Nadia Brancati, Maria Frucci, Daniel Riccio, Jean-Philippe Thiran, Orcun Goksel, and Maria Gabrani: "Graph Representation Learning and Explainability in Breast Cancer Pathology: Bridging the Gap between AI and Pathology Practice", In Artificial Intelligence Applications In Human Pathology, pp. 243-285, World Scientific Publishing Europe, 2022.
@incollection{Pati_graph_22, author = {Pushpak Pati and Guillaume Jaume and Antonio Foncubierta-Rodriguez and Florinda Feroce and Giosue Scognamiglio and Anna Maria Anniciello and Nadia Brancati and Maria Frucci and Daniel Riccio and Jean-Philippe Thiran and Orcun Goksel and Maria Gabrani}, title = {Graph Representation Learning and Explainability in Breast Cancer Pathology: Bridging the Gap between AI and Pathology Practice}, booktitle = {Artificial Intelligence Applications In Human Pathology}, publisher = {World Scientific Publishing Europe}, year = {2022}, pages = {243-285}, doi = {10.1142/9781800611399_0010} }
[2017] Orcun Goksel and Antonio Foncubierta-Rodr\iguez: "VISCERAL Anatomy Benchmarks for Organ Segmentation and Landmark Localization: Tasks and Results", In Cloud-Based Benchmarking of Medical Image Analysis, pp. 107-125, Springer, 2017.
@incollection{Goksel_visceral_17, author = {Goksel, Orcun and Foncubierta-Rodr{\'\i}guez, Antonio}, title = {VISCERAL Anatomy Benchmarks for Organ Segmentation and Landmark Localization: Tasks and Results}, booktitle = {Cloud-Based Benchmarking of Medical Image Analysis}, publisher = {Springer}, year = {2017}, pages = {107--125}, doi = {10.1007/978-3-319-49644-3_7} }
[2016] Philipp Fürnstahl, Andreas Schweizer, Matthias Graf, Lazaros Vlachopoulos, Sandro Fucentese, Stephan Wirth, Ladislav Nagy, Gabor Szekely, and Orcun Goksel: "Surgical Treatment of Long-bone Deformities: 3D Preoperative Planning and Patient-specific Instrumentation", In Computational Radiology for Orthopaedic Interventions, ed. Guoyan Zheng and Shuo Li, pp. 123-149, Springer, 2016.
of motion, joint instability, pain, and osteoarthritis. The conventional
joint-preserving therapy for such deformities is corrective osteotomy
-- the anatomical reduction or realignment of bones with fixation.
In this procedure, the bone is cut and its fragments are correctly
realigned and stabilized with an implant to secure their position
during bone healing. Corrective osteotomy is an elective procedure
scheduled in advance, providing sufficient time for careful diagnosis
and operation planning. Accordingly, computer-based methods have
become very popular for its preoperative planning. These methods
can improve precision not only by enabling the surgeon to quantify
deformities and to simulate the intervention preoperatively in three
dimensions, but also by generating a surgical plan of the required
correction. However, generation of complex surgical plans is still
a major challenge, requiring sophisticated techniques and profound
clinical expertise. In addition to preoperative planning, computer-based
approaches can also be used to support surgeons during the course
of interventions. In particular, since recent advances in additive
manufacturing technology have enabled cost-effective production of
patient- and intervention-specific osteotomy instruments, customized
interventions can thus be planned for and performed using such instruments.
In this chapter, state of the art and future perspectives of computer-assisted
deformity-correction surgery of the upper and lower extremities are
presented. We elaborate on the benefits and pitfalls of different
approaches based on our own experience in treating over 150 patients
with three-dimensional preoperative planning and patient-specific
instrumentation.
@incollection{Fuernstahl_surgical_16, author = {Philipp F\"urnstahl and Andreas Schweizer and Matthias Graf and Lazaros Vlachopoulos and Sandro Fucentese and Stephan Wirth and Ladislav Nagy and Gabor Szekely and Orcun Goksel}, title = {Surgical Treatment of Long-bone Deformities: 3D Preoperative Planning and Patient-specific Instrumentation}, booktitle = {Computational Radiology for Orthopaedic Interventions}, editor = {Guoyan Zheng and Shuo Li}, publisher = {Springer}, year = {2016}, pages = {123-149}, url = {http://www.springer.com/us/book/9783319234816}, doi = {10.1007/978-3-319-23482-3_7} }
[2014] Orçun Göksel and Gabor Székely: "Computational Support for Intraoperative Imaging and IGT", In Intraoperative Imaging and Image-Guided Therapy, ed. Ferenc A. Jolesz, pp. 63-77, Springer, New York, Jan 2014.
in which they play a decisive role, even if they are often invisible,
seamlessly integrated into our surrounding and the objects we use
every day. Image-guided therapy is certainly not an exception to
this. Nowadays practically all imaging devices rely on computer support,
commonly used for the purpose of controlling medical devices, for
post-processing acquired raw data to turn them into images, and for
transmitting resulting digital data for storage or further use. Furthermore,
many therapeutic devices are equipped with sophisticated sensors
and actuators, which are also controlled by computers. Indeed, software
support today is an intrinsic component of all phases of therapy.
In this chapter, we provide an overview of tools and technologies
that are used as the building blocks of almost every computational
support system in image-guided therapy. The tools introduced in this
chapter include segmentation, registration, localization, simulation,
model generation, visualization, robotic tools, and man-machine interfaces.
The use of such tools in preoperative planning and intraoperative
surgical support is then described and exemplified on typical treatment
scenarios.
@incollection{Goksel_computational_14, author = {Or\c{c}un G\"oksel and Gabor Sz\'ekely}, title = {Computational Support for Intraoperative Imaging and IGT}, booktitle = {Intraoperative Imaging and Image-Guided Therapy}, editor = {Ferenc A. Jolesz}, publisher = {Springer}, year = {2014}, pages = {63-77}, doi = {10.1007/978-1-4614-7657-3_4} }
[2012] Septimiu E. Salcudean, Ramin S. Sahebjavaher, Orcun Goksel, Ali Baghani, Sara S. Mahdavi, Guy Nir, Ralph Sinkus, and Mehdi Moradi: "Biomechanical Modeling of the Prostate for Procedure Guidance and Simulation", In Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, ed. Yohan Payan, pp. 169-198, v 11, Springer, Heidelberg, 2012.
in the diagnosis and management of prostate cancer. Most importantly,
it has been shown in several studies that cancerous prostate tissue
has different viscoelastic properties than normal prostate tissue:
it is typically stiffer (higher storage modulus) and more viscous
(higher loss modulus). If a strong correlation can be obtained between
malignant tissue and its viscoelastic properties, then all commonly
practiced prostate cancer procedures/biopsies, surgery and radiation
treatment can be improved by elasticity imaging. The elastic properties
of the prostate and peri-prostatic tissue can also be used in procedure
planning, even if such elastic properties do not show strong correlation
to cancer. This chapter starts with an introduction to the prostate
anatomy, prostate cancer, and a description of the most common procedures
and their clinical needs. It continues by presenting the potential
impact of elasticity imaging on these procedures. A brief survey
of elastography techniques is presented next, with a sampling of
some prostate elastography results to date. We describe two of the
systems that we developed for the acquisition of prostate ultrasound
and magnetic resonance elastography images and summarize our results
to date. We show that these elasticity images can be used for prostate
segmentation and cross-modality image registration. Furthermore,
we show how prostate region deformation models can be used in the
development of a prostate brachytherapy simulator which can also
be used in the planning of needle insertions that account for deformation.
@incollection{Salcudean_biomechanical_12, author = {Septimiu E. Salcudean and Ramin S. Sahebjavaher and Orcun Goksel and Ali Baghani and Sara S. Mahdavi and Guy Nir and Ralph Sinkus and Mehdi Moradi}, title = {Biomechanical Modeling of the Prostate for Procedure Guidance and Simulation}, booktitle = {Soft Tissue Biomechanical Modeling for Computer Assisted Surgery}, editor = {Yohan Payan}, publisher = {Springer}, year = {2012}, volume = {11}, pages = {169-198}, doi = {10.1007/978-3-642-29014-5} }
[2015] Orcun Goksel, Antonio Foncubierta-Rodríguez, Oscar Alfonso Jiménez del Toro, Henning Müller, Georg Langs, Marc-André Weber, Bjoern Menze, Ivan Eggel, Katharina Gruenberg, Marianne Winterstein, Markus Holzer, Markus Krenn, Georgios Kontokotsios, Sokratis Metallidis, Roger Schaer, Abdel Aziz Taha, András Jakab, Tomàs Salas Fernandez, and Allan Hanbury: "Overview of the VISCERAL Challenge at ISBI 2015", In Proceedings of VISCERAL Challenge at ISBI, ed. Orcun Goksel et al., pp. 6-11 (1390), CEUR-WS, Apr 2015.
of the VISCERAL Segmentation Challenge at ISBI 2015. The challenge
was organized on a cloud-based virtualmachine environment, where
each participant could develop and submit their algorithms. The dataset
contains up to 20 anatomical structures annotated in a training and
a test set consisting of CT and MR images with and without contrast
enhancement. The test-set is not accessible to participants, and
the organizers run the virtual-machines with submitted segmentation
methods on the test data. The results of the evaluation are then
presented to the participant, who can opt to make it public on the
challenge leaderboard displaying 20 segmentation quality metrics
per-organ and permodality. Dice coefficient and mean-surface distance
are presented herein as representative quality metrics. As a continuous
evaluation platform, our segmentation challenge leaderboard will
be open beyond the duration of the VISCERAL project.
@incollection{Goksel_overview_15, author = {Orcun Goksel and Antonio Foncubierta--Rodr\'iguez and Oscar Alfonso Jim\'enez del Toro and Henning M\"uller and Georg Langs and Marc-Andr\'e Weber and Bjoern Menze and Ivan Eggel and Katharina Gruenberg and Marianne Winterstein and Markus Holzer and Markus Krenn and Georgios Kontokotsios and Sokratis Metallidis and Roger Schaer and Abdel Aziz Taha and Andr\'as Jakab and Tom\`as Salas Fernandez and Allan Hanbury}, title = {Overview of the VISCERAL Challenge at ISBI 2015}, booktitle = {Proceedings of VISCERAL Challenge at ISBI}, editor = {Orcun Goksel et al.}, publisher = {CEUR-WS}, year = {2015}, number = {1390}, pages = {6--11}, url = {http://ceur-ws.org/Vol-1390/visceralISBI15-0.pdf} }
[2014] Oscar Alfonso Jiménez del Toro, Orcun Goksel, Bjoern Menze, Henning Müller, Georg Langs, Marc-André Weber, Ivan Eggel, Katharina Gruenberg, Markus Holzer, András Jakab, Georgios Kotsios-Kontokotsios, Markus Krenn, Tomàs Salas Fernandez, Roger Schaer, Abdel Aziz Taha, Marianne Winterstein, and Allan Hanbury: "VISCERAL - VISual Concept Extraction challenge in RAdioLogy : ISBI 2014 Challenge Organization", In VISCERAL Challenge at ISBI, ed. Orcun Goksel, pp. 6-15 (1194), CEUR-WS, May 2014.
has been developed as a cloud-based infrastructure for the evaluation
of medical image data in large data sets. As part of this project,
the ISBI 2014 (International Symposium for Biomedical Imaging) challenge
was organized using the VISCERAL data set and shared cloud-framework.
Two tasks were selected to exploit and compare multiple state-of-the-art
solutions designed for big data medical image analysis. Segmentation
and landmark localization results from the submitted algorithms were
compared to manually annotated ground truth in the VISCERAL data
set. This paper presents an overview of the challenge setup and data
set used as well as the evaluation metrics from the various results
submitted to the challenge. The participants presented their algorithms
during an organized session at ISBI 2014. There were lively discussions
in which the importance of comparing approaches on tasks sharing
a common data set was highlighted.
@incollection{Jimenez_visceral_14, author = {Jim\'enez del Toro, Oscar Alfonso and Orcun Goksel and Bjoern Menze and Henning M\"uller and Georg Langs and Marc-Andr\'e Weber and Ivan Eggel and Katharina Gruenberg and Markus Holzer and Andr\'as Jakab and Georgios Kotsios-Kontokotsios and Markus Krenn and Tom\`as Salas Fernandez and Roger Schaer and Abdel Aziz Taha and Marianne Winterstein and Allan Hanbury}, title = {VISCERAL - VISual Concept Extraction challenge in RAdioLogy : ISBI 2014 Challenge Organization}, booktitle = {VISCERAL Challenge at ISBI}, editor = {Orcun Goksel}, publisher = {CEUR-WS}, year = {2014}, number = {1194}, pages = {6-15}, url = {http://ceur-ws.org/Vol-1194/visceralISBI14-0.pdf} }
Conference Proceedings:

@inproceedings{Bezek_global_22, author = {Can Deniz Bezek and Mert Bilgin and Lin Zhang and Orcun Goksel}, title = {Global Speed-of-Sound Prediction Using Transmission Geometry}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2022}, number = {arxiv:2208.08377}, url = {https://arxiv.org/abs/2208.08377} }

@inproceedings{Thandiackal_differentiable_22, author = {Kevin Thandiackal and Boqi Chen and Pushpak Pati and Guillaume Jaume and Drew FK Williamson and Maria Gabrani and Orcun Goksel}, title = {Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2022}, url = {https://arxiv.org/abs/2204.12454} }

@inproceedings{Gomariz_unsupervised_22, author = {Alvaro Gomariz and Huanxiang Lu and Yun Yvonna Li and Thomas Albrecht and Andreas Maunz and Fethallah Benmansour and Alessandra M Valcarcel and Jennifer Luu and Daniela Ferrara and Orcun Goksel}, title = {Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation}, booktitle = {MICCAI}, year = {2022}, url = {https://arxiv.org/abs/2203.03664} }

@inproceedings{Gomariz_unified_22, author = {Alvaro Gomariz and Huanxiang Lu and Yun Li and Thomas Albrecht and Andreas Maunz and Fethallah Benmansour and Jennifer Luu and Orcun Goksel and Daniela Ferrara}, title = {A unified deep learning approach for OCT segmentation from different devices and retinal diseases}, booktitle = {ARVO Annual Meeting Procs in Investigative Ophthalmology and Visual Science}, year = {2022}, volume = {63}, number = {7}, pages = {2053-F0042}, url = {https://iovs.arvojournals.org/article.aspx?articleid=2782139} }
2021

@inproceedings{Tomar_content-preserving_21, author = {Devavrat Tomar and Lin Zhang and Tiziano Portenier and Orcun Goksel}, title = {Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images}, booktitle = {MICCAI}, year = {2021}, url = {https://arxiv.org/abs/2103.05745}, doi = {10.1007/978-3-030-87237-3_63} }

@inproceedings{Anklin_learning_21, author = {Valentin Anklin and Pushpak Pati and Guillaume Jaume and Behzad Bozorgtabar and Antonio Foncubierta-Rodriguez and Jean-Philippe Thiran and Mathilde Sibony and Maria Gabrani and Orcun Goksel}, title = {Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs}, booktitle = {MICCAI}, year = {2021}, pages = {636-646}, url = {https://arxiv.org/abs/2103.03129}, doi = {10.1007/978-3-030-87196-3_59} }

@inproceedings{Augustin_estimating_21, author = {Xenia Augustin and Lin Zhang and Orcun Goksel}, title = {Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2021}, url = {https://arxiv.org/abs/2109.11819}, doi = {10.1109/IUS52206.2021.9593742} }

@inproceedings{Jaume_quantifying_21, author = {Guillaume Jaume and Pushpak Pati and Behzad Bozorgtabar and Antonio Foncubierta-Rodr'iguez and Florinda Feroce and Anna Maria Anniciello and Tilman Rau and Jean-Philippe Thiran and Maria Gabrani and Orcun Goksel}, title = {Quantifying Explainers of Graph Neural Networks in Computational Pathology}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, pages = {8102-12}, url = {https://arxiv.org/abs/2011.12646}, doi = {10.1109/CVPR46437.2021.00801} }

@inproceedings{Gomariz_utilizing_21, author = {Alvaro Gomariz and Raphael Egli and Tiziano Portenier and C\'esar Nombela-Arrieta and Orcun Goksel}, title = {Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers}, booktitle = {IEEE Int Symposium on Biomedical Imaging (ISBI)}, year = {2021}, pages = {371-4}, url = {https://arxiv.org/abs/2101.11476}, doi = {10.1109/ISBI48211.2021.9434158} }

@inproceedings{Chintada_time_21, author = {Bhaskara Rao Chintada and Richard Rau and Orcun Goksel}, title = {Time of Arrival Delineation In Echo Traces For Reflection Ultrasound Tomography}, booktitle = {IEEE Int Symposium on Biomedical Imaging (ISBI)}, year = {2021}, pages = {1342-5}, url = {pub/Chintada_time_21pre.pdf}, doi = {10.1109/ISBI48211.2021.9433846} }
2020

@inproceedings{Portenier_gramgan_20, author = {Tiziano Portenier and Siavash Bigdeli and Orcun Goksel}, title = {GramGAN: Deep 3D Texture Synthesis From 2D Exemplars}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2020}, pages = {1-11}, url = {https://arxiv.org/abs/2006.16112} }

* Best Paper Award
@inproceedings{Pati_hact-net_20, author = {Pushpak Pati and Guillaume Jaume and Lauren Alisha Fernandes and Antonio Foncubierta-Rodriguez and Florinda Feroce and Anna Maria Anniciello and Giosue Scognamiglio and Nadia Brancati and Daniel Riccio and Maurizio Di Bonito and Giuseppe De Pietro and Gerardo Botti and Orcun Goksel and Jean-Philippe Thiran and Maria Frucci and Maria Gabrani}, title = {HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification}, booktitle = {MICCAI Workshop on Graphs in Biomedical Image Analysis (GRAIL)}, year = {2020}, pages = {208-219}, url = {https://arxiv.org/abs/2007.00584}, doi = {10.1007/978-3-030-60365-6_20} }

@inproceedings{Joos_recurrent_20, author = {Emanuel Joos and Fabien P\'ean and Orcun Goksel}, title = {Reinforcement Learning of Musculoskeletal Control from Functional Simulations}, booktitle = {MICCAI}, year = {2020}, pages = {135-145}, url = {https://arxiv.org/abs/2007.06669}, doi = {10.1007/978-3-030-59716-0_14} }

@inproceedings{Zhang_deepI_20, author = {Lin Zhang and Tiziano Portenier and Christoph Paulus and Orcun Goksel}, title = {Deep Image Translation for Enhancing Simulated Ultrasound Images}, booktitle = {MICCAI Workshop on Advances in Simplifying Medical Ultrasound}, year = {2020}, pages = {85-94}, url = {https://arxiv.org/abs/2006.10850}, doi = {10.1007/978-3-030-60334-2_9} }

@inproceedings{Rau_displacement_20, author = {Richard Rau and Ece Ozkan and Batu M. Ozturkler and Leila Gastli and Orcun Goksel}, title = {Displacement Estimation Methods for Speed-of-Sound Imaging in Pulse-Echo}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2020}, pages = {2007-2010}, doi = {10.1109/IUS46767.2020.9251781} }

@inproceedings{Rau_functional_20, author = {Richard Rau and Justine Robin and Berkan Lafci and Aileen Schroeter and Michael Reiss and X Luis Dean Ben and Orcun Goksel and Daniel Razansky}, title = {Functional Neuroimaging of Mice Using Ultrasound and Optoacoustics}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2020}, pages = {1} }

@inproceedings{Chintada_model-independent_20, author = {Bhaskara Rao Chintada and Richard Rau and Orcun Goksel}, title = {Model-Independent Quantification of Complex Shear Modulus via Speed and Attenuation of Surface Waves}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2020}, pages = {1269-1272}, url = {pub/Chintada_model-independent_20.pdf}, doi = {10.1109/IUS46767.2020.9251721} }

@inproceedings{Jaume_towards_20, author = {Guillaume Jaume and Pushpak Pati and Antonio Foncubierta-Rodriguez and Florinda Feroce and Giosue Scognamiglio and Anna Maria Anniciello and Jean-Philippe Thiran and Orcun Goksel and Maria Gabrani}, title = {Towards Explainable Graph Representations in Digital Pathology}, booktitle = {ICML Workshop on Computational Biology}, year = {2020}, pages = {1-5}, url = {https://arxiv.org/abs/2007.00311} }

@inproceedings{Pati_mitosis_20, author = {Pushpak Pati and Antonio Foncubierta-Rodr\'{i}guez and Orcun Goksel and Maria Gabrani}, title = {Mitosis Detection under Limited Annotation: A Joint Learning Approach}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2020}, pages = {486-489}, url = {https://arxiv.org/abs/2006.09772}, doi = {10.1109/ISBI45749.2020.9098431} }
2019

* Received MICCAI Best Presentation Award (1730 paper submissions in MICCAI 2019)
@inproceedings{Rau_attenuation_19, author = {Rau, Richard and Ozan Unal and Dieter Schweizer and Valery Vishnevskiy and Orcun Goksel}, title = {Attenuation Imaging with Pulse-Echo Ultrasound based on an Acoustic Reflector}, booktitle = {MICCAI}, year = {2019}, pages = {601-609}, url = {https://arxiv.org/abs/1906.11615}, doi = {10.1007/978-3-030-32254-0_67} }

* Among 15 Finalists for MICCAI Best Presentation Award (1730 paper submissions to MICCAI 2019)
@inproceedings{Vishnevskiy_deep_19, author = {Valery Vishnevskiy and Richard Rau and Orcun Goksel}, title = {Deep Variational Networks with Exponential Weighting for Learning Computed Tomography}, booktitle = {MICCAI}, year = {2019}, pages = {310-318}, url = {https://arxiv.org/abs/1906.05528}, doi = {10.1007/978-3-030-32226-7_35} }

@inproceedings{Rau_ultrasound_19, author = {Richard Rau and Dieter Schweizer and Valery Vishnevskiy and Orcun Goksel}, title = {Ultrasound Aberration Correction based on Local Speed-of-Sound Map Estimation}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2019}, pages = {1-4}, url = {https://arxiv.org/abs/1909.10254} }

@inproceedings{Chintada_acousto_19, author = {Bhaskara Rao Chintada and Richard Rau and Orcun Goksel}, title = {Acoustoelasticity analysis of shear waves for nonlinear biomechanical characterization of oil-gelatin phantoms}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2019}, pages = {423-6}, url = {https://www.research-collection.ethz.ch/handle/20.500.11850/403888}, doi = {10.1109/ULTSYM.2019.8925670} }

@inproceedings{AlBahou_scatgan_19, author = {Al Bahou, Andrawes and Christine Tanner and Orcun Goksel}, title = {ScatGAN for Reconstruction of Ultrasound Scatterers using Generative Adversarial Networks}, booktitle = {IEEE Int Symposium on Biomedical Imaging (ISBI)}, year = {2019}, pages = {1674-7}, url = {https://arxiv.org/abs/1902.00469}, doi = {10.1109/ISBI.2019.8759251} }

@inproceedings{Zhang_implicit_19, author = {Lin Zhang and Valery Vishnevsky and Andras Jakab and Orcun Goksel}, title = {Implicit modeling with uncertainty estimation for intravoxel incoherent motion imaging}, booktitle = {IEEE Int Symposium on Biomedical Imaging (ISBI)}, year = {2019}, pages = {1003-7}, url = {https://arxiv.org/abs/1810.10358}, doi = {10.1109/ISBI.2019.8759391} }

@inproceedings{Gomariz_siamese_19, author = {Alvaro Gomariz and Weiye Li and Ece Ozkan and Christine Tanner and Orcun Goksel}, title = {Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences}, booktitle = {IEEE Int Symposium on Biomedical Imaging (ISBI)}, year = {2019}, pages = {1757-1760}, url = {https://arxiv.org/abs/1901.08109}, doi = {10.1109/ISBI.2019.8759382} }

@inproceedings{Ciganovic_deep_19, author = {Matija Ciganovic and Firat Ozdemir and Mazda Farshad and Orcun Goksel}, title = {Deep learning techniques for bone surface delineation in ultrasound}, booktitle = {SPIE Medical Imaging}, year = {2019}, pages = {10955-33}, url = {https://www.research-collection.ethz.ch/handle/20.500.11850/350278}, doi = {10.1117/12.2512997} }

@inproceedings{Pati_deep_19, author = {Pushpak Pati and Raul Catena and Orcun Goksel and Maria Gabrani}, title = {A deep learning framework for context-aware mitotic activity estimation in whole slide images}, booktitle = {SPIE Medical Imaging}, year = {2019}, pages = {10956-09}, doi = {10.1117/12.2512705} }
2018

@inproceedings{Vishnevskiy_image_18, author = {Valery Vishnevskiy and Sergio J Sanabria and Orcun Goksel}, title = {Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging}, booktitle = {MICCAI Workshop on Machine Learning for Medical Image Reconstruction}, year = {2018}, pages = {120-128}, url = {https://arxiv.org/abs/1807.07416}, doi = {10.1007/978-3-030-00129-2_14} }

@inproceedings{Ozdemir_learn_18, author = {Firat Ozdemir and Philipp Fuernstahl and Orcun Goksel}, title = {Learn the New, Keep the Old: Extending Pretrained Models with New Anatomy and Images}, booktitle = {MICCAI}, year = {2018}, pages = {361-369}, url = {https://arxiv.org/abs/1806.00265}, doi = {10.1007/978-3-030-00937-3_42} }

@inproceedings{Tanner_combining_18, author = {Christine Tanner and Rastislav Starkov and Michael Bajka and Orcun Goksel}, title = {Framework for Fusion of Data- and Model-Based Approaches for Ultrasound Simulation}, booktitle = {MICCAI}, year = {2018}, pages = {332-339}, doi = {10.1007/978-3-030-00937-3_39} }

@inproceedings{Ozdemir_active_18, author = {Firat Ozdemir and Zixuan Peng and Christine Tanner and Philipp Fuernstahl and Orcun Goksel}, title = {Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy}, booktitle = {MICCAI Workshop on Deep Learning in Medical Image Analysis}, year = {2018}, pages = {183-191}, url = {https://arxiv.org/abs/1807.07349}, doi = {10.1007/978-3-030-00889-5_21} }

@inproceedings{Thandiackal_structure-aware_18, author = {Kevin Thandiackal and Orcun Goksel}, title = {A Structure-aware Convolutional Neural Network for Skin Lesion Classification}, booktitle = {MICCAI Workshop on ISIC Skin Image Analysis}, year = {2018}, pages = {312-319}, doi = {10.1007/978-3-030-01201-4_34} }

@inproceedings{Otesteanu_ultrasound_18, author = {Corin F. Otesteanu and Valeriy Vishnevskiy and Christian Guenthner and Sebastian Kozerke and Orcun Goksel}, title = {Ultrasound and Magnetic-Resonance Harmonic Elastography with Hybrid Inverse-Problem Formulation of FEM Viscoelasticity Reconstruction}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2018} }

Methods: Twenty healthy women with a regular MC were scanned twice with a two-week period in between using a standard US machine and a reflector, which served as a timing reference. Three of these volunteers were scanned twice a week for a period of four weeks. According to their menstrual history, the women were allocated to either the follicular or luteal group. Average SoS (m/s) and ?SoS (variation SoS; m/s) were assessed in the retromamillary, inner and outer segments of both breasts to determine the breast density. Spearman?s rank correlation coefficient rs was calculated to evaluate correlations between breast SoS/?SoS and patient as well as measured characteristics. Interexaminer and interreader agreements were assessed with the interclass correlation coefficient (ICC). ANOVA and Tukey?s test were used to assess differences within the breast as well as changes over the four-week period. Comparisons of the follicular and luteal phases, the acquisitions before and during compression as well as the left and right breast were performed with a student t-test.
Results: SoS values showed an inverse correlation with BMI (rs=-0.772, p<0.001), body weight (rs=-0.718, p<0.001), age (rs=-0.659, p<0.01) and bra cup size (rs=-0.494, p<0.05). For SoS, the interreader (ICC=0.990) and interexaminer (ICC=0.979) agreements were very high. SoS showed a positive correlation (rs=0.956, p<0.001) between the left and right breasts without a statistically significant difference comparing the two acquisitions (p=0.123). For SoS, the comparison before and during compression was not significant (p=0.639) and positively correlated (rs=0.885, p<0.001). There was no statistically significant difference between the SoS of the follicular and luteal phases (ANOVA p<0.001). SoS of the inner breast segments were significantly lower compared to the middle (p<0.001) and outer segments (p<0.001).
Conclusions: This result implies that the diagnostic validity of the SoS-US is not significantly affected by the timing of the scan during the menstrual cycle. Interexaminer- and interreader accordance were high and measurements not influenced by probe pressure.
@inproceedings{Ruby_menstrual_18, author = {Lisa Ruby and Sergio J Sanabria and Anika S Obrist and Katharina Martini and Serafino Forte and Orcun Goksel and Thomas Frauenfelder and Rahel Kubik-Huch and Marga B Rominger}, title = {Menstrual cycle-related changes in breast density using hand-held Speed-of-Sound Ultrasound}, booktitle = {Dreiländertreffen Ultraschall}, journal = {Ultraschall in der Medizin}, year = {2018}, volume = {39}, pages = {S43-4}, doi = {10.1055/s-0038-1670479} }

@inproceedings{Jakab_neuroshape_18, author = {A. Jakab and Z. Goey and O. Goksel and R. Tuura and R. Bauer and L. Stieglitz and G. Szekely and E. Martin and B. Werner}, title = {NeuroShape: a novel software implementing state-of-the-art targeting for MRI-guided focused ultrasound neurosurgery}, booktitle = {Swiss Congress of Radiology}, year = {2018}, doi = {10.13140/RG.2.2.16998.73283} }

@inproceedings{Pean_musculoskeletal_18, author = {Fabien P\'{e}an and Philipp F\u{u}rnstahl and Orcun Goksel}, title = {A musculoskeletal model of the shoulder combining multibody dynamics and FEM using B-Spline elements}, booktitle = {Int Symp Computer Methods in Biomechanics and Biomedical Engineering (CMBBE)}, year = {2018}, url = {http://cmbbe2018.tecnico.ulisboa.pt/pen_cmbbe2018/pdf/WEB_ABSTRACTS/Abstracts_CMBBE2018_149.pdf} }

@inproceedings{Sanabria_breast_18, author = {Sergio Sanabria and Katharina Martini and Marga Rominger and Konstantin Dedes and D. Vorburger and Thomas Frauenfelder and Orcun Goksel}, title = {Breast cancer assessment with hand-held ultrasound based Speed of Sound: preliminary results}, booktitle = {European Conference on Radiology (ECR)}, year = {2018}, doi = {10.1594/ecr2018/C-1388} }

@inproceedings{Metzger_sarcopenia_18, author = {M-L Metzger and Gregor Freyst\"{a}tter and Katharina Martini and Thomas Frauenfelder and Orcun Goksel and Sergio J Sanabria and Marga B. Rominger}, title = {Sarcopenia assessment with handheld ultrasound: a novel technique based on speed of sound}, booktitle = {European Conference on Radiology (ECR)}, year = {2018}, doi = {10.1594/ecr2018/C-1211} }

@inproceedings{Martini_hand-held_18, author = {Katharina Martini and Sergio J Sanabria and Serafino Forte and Rahel A Kubik-Huch and Thomas Frauenfelder and Orcun Goksel and Marga B. Rominger}, title = {Hand-held speed-of-sound ultrasound -- a novel cost-efficient biomarker for breast density assessment}, booktitle = {European Conference on Radiology (ECR)}, year = {2018}, doi = {10.1594/ecr2018/C-0037} }
2017

@inproceedings{Sanabria_can_17, author = {Sergio J Sanabria and Marga B Rominger and Corin F Otesteanu and Farrukh I Sheikh and Volker Klingmueller and Orcun Goksel}, title = {Can speed of sound be better than conventional elastography for breast characterization?}, booktitle = {Annual Meeting of Radiological Society of North America (RSNA)}, year = {2017} }

@inproceedings{Farrukh_interpreting_17, author = {Waleed Farrukh and Antonio Foncubierta-Rodriguez and Anca-Nicoleta Ciubotaru and Guillaume Jaume and Costas Bekas and Orcun Goksel and Maria Gabrani}, title = {Interpreting data from scanned tables}, booktitle = {GREC 2017: 12th IAPR International Workshop on Graphics Recognition}, year = {2017}, doi = {10.1109/ICDAR.2017.250} }

@inproceedings{Pati_computational_17, author = {Pushpak Pati and Murat Arar and Govind Kaigala and Kashyap Aditya and Orcun Goksel and Maria Gabrani}, title = {Computational Immunohistochemistry: Recipes for Standardization of Immunostaining}, booktitle = {MICCAI}, year = {2017}, doi = {10.1007/978-3-319-66185-8_6} }

@inproceedings{Otesteanu_quantification_17, author = {Corin F. Otesteanu and Bhaskara R. Chintada and Edoardo Mazza and Sergio J. Sanabria and Orcun Goksel}, title = {Quantification of nonlinear elastic constants using polynomials in quasi-incompressible soft solids}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2017}, doi = {10.1109/ULTSYM.2017.8091737} }

@inproceedings{Chintada_reflector-based_17, author = {Bhaskara R. Chintada and Sergio J. Sanabria and Wolfgang Bost and Orcun Goksel}, title = {Reflector-Based 3D Tomographic Ultrasound Reconstruction: Simulation Study}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2017}, doi = {10.1109/ULTSYM.2017.8092257} }

@inproceedings{Pean_physical_17, author = {Fabien Pean and Fabio Carrillo and Philipp Fuernstahl and Orcun Goksel}, title = {Physical Simulation of the Interosseous Ligaments During Forearm Rotation}, booktitle = {Computer Assisted Orthopaedic Surgery}, year = {2017}, url = {https://easychair.org/publications/paper/350665}, doi = {10.29007/74wg} }

Adult bone marrow (BM) cavities host continuous, demand adapted and high throughput blood cell production, which is maintained by a rare population of self-renewing, multipotent hematopoietic stem cells (HSCs). Aside from its diverse hematopoietic content, the BM is populated by a heterogeneous fraction of mesenchymal, endothelial and neural stromal cells, which provide the necessary tissue infrastructure for hematopoiesis to unfold while playing fundamental regulatory roles in hematopoietic development. Recent evidence suggests that tissue regions around BM venous microvessels (termed sinusoids), which are enriched for mesenchymal CXCL12-abundant reticular cells (CARc), serve as the principal regulatory niches for HSCs as well as other hematopoietic progenitor populations. Despite this proposed role as putative specific niche-restricted components, comprehensive data on the frequency, global spatial distribution and topology of sinusoidal endothelial and CAR cell networks is largely lacking to date.
Aims
The principal aim of our work is to employ state of the art imaging techniques to perform a detailed 3D quantitative and structural analysis of the BM stromal infrastructure, with a special focus on sinusoidal microvasculature and the CAR cell mesenchymal component, both of which are essential regulators of HSC maintenance.
Methods
We have developed i) advanced microscopy techniques allowing multiscale 3D visualization of entire bone marrow cavities with cellular and subcellular detail ii) customized computational tools enabling the detection and quantification of discrete cell subsets/structures in 3D images of the BM in an unbiased fashion, as well as a rigorous spatial statistical analysis of cellular interactions.
Results
Using 3D-quantitative microscopy (3D-QM) we uncover that BM stromal cells are in fact 15-20 fold more abundant than previously reported. The massive underestimation of these relevant cell subsets results from the highly inefficient isolation of these cellular types with currently employed flow cytometry protocols. Our image-based analyses further reveals that sinusoidal and CAR cell stromal networks occupy a disproportionately large fraction of the BM space, consequently constraining the tissue volume available for hematopoietic cell distribution. In fact, the vast majority of BM resident hematopoietic cells are unavoidably in direct contact with the CAR cellular projections and in close proximity (<25um) to the extraluminal surface of sinusoidal endothelium.
Conclusion
Collectively, our quantitative description of stromal microarchitecture, challenges current models of cell type-specific niche interactions in the BM, which are based in largely inaccurate estimations of cell frequency and spatial confinement of stromal cells in this organ.
@inproceedings{Gomariz_multiscale_17, author = {Gomariz, A and Helbling, P and Isringhausen, S and Suessbier, U and Becker, A and Boss, A and Nagasawa, T and Paul, G and Goksel, O and Szekely, G and others}, title = {Multiscale Image-Based Quantitative Analysis of Bone Marrow Stromal Network Topology Reveals Strict Spatial Constraints for Hematopoietic-Stromal Cellular Interactions}, booktitle = {Haematologica}, year = {2017}, volume = {102}, pages = {78--79} }

is the plausible simulation of the characteristic noise pattern known as
ultrasonic speckle. The formation of ultrasonic speckle can be approximated
efficiently by convolving the ultrasound point-spread function
(PSF) with a distribution of point scatterers. Recent work extracts the
latter directly from ultrasound images for use in forward simulation, assuming
that the PSF can be known, e.g., from experiments. In this paper,
we investigate the problem of automatically estimating an unknown PSF
for the purpose of ultrasound simulation, such as to use in convolution-based
ultrasound image formation. Our method estimates the PSF directly
from an ultrasound image, based on homomorphic filtering in the
cepstrum domain. It robustly captures local changes in the PSF as a
function of depth, and hence is able to reproduce continuous ultrasound
beam profiles. We compare our method to numerical simulations as the
ground truth to study PSF estimation accuracy, achieving small approximation
errors of 15% FWHM. We also demonstrate simulated in-vivo
images, with beam profiles estimated from real images.
@inproceedings{Mattausch_comparison_17, author = {Oliver Mattausch and Elisabeth Ren and Michael Bajka and Kenneth Vanhoey and Orcun Goksel}, title = {Comparison of Texture Synthesis Methods for Content Generation in Ultrasound Training Simulation}, booktitle = {SPIE Medical Imaging}, year = {2017}, pages = {1-8}, doi = {10.1117/12.2250604} }
2016

@inproceedings{Goksel_abdominal_16, author = {Orcun Goksel and Valeria DeLuca and Maxim Makhinya and Christine Tanner}, title = {Abdominal Motion Tracking and Prediction in 2D Ultrasound}, booktitle = {4D Treatment Planning (4DTP) Workshop}, year = {2016} }

@inproceedings{Vishnevskiy_accurate_16, author = {Valeriy Vishnevskiy and Christine Tanner and Orcun Goksel}, title = {Accurate and Fast Deformable Image Registration Allowing For Sliding Interfaces}, booktitle = {4D Treatment Planning (4DTP) Workshop}, year = {2016} }

@inproceedings{Sanabria_hand-held_16, author = {Sergio J. Sanabria and Orcun Goksel}, title = {Hand-held Sound-Speed Mammography Based on Ultrasound Reflector Tracking}, booktitle = {MICCAI}, year = {2016}, pages = {568-576}, doi = {10.1007/978-3-319-46720-7_66} }

@inproceedings{Ozdemir_graphical_16, author = {Firat Ozdemir and Ece Ozkan and Orcun Goksel}, title = {Graphical Modeling of Ultrasound Propagation in Tissue for Automatic Bone Segmentation}, booktitle = {MICCAI}, year = {2016}, pages = {256-264}, doi = {10.1007/978-3-319-46723-8_30} }

@inproceedings{Tanner_4D_16, author = {Christine Tanner and Celine Eggenberger and Barbara Flach and Oliver Mattausch and Michael Bajka and Orcun Goksel}, title = {4D Reconstruction of Fetal Heart Ultrasound Images in Presence of Fetal Motion}, booktitle = {MICCAI}, year = {2016}, pages = {593-601}, doi = {10.1007/978-3-319-46720-7_69} }

@inproceedings{Mattausch_image-based_16, author = {Oliver Mattausch and Orcun Goksel}, title = {Image-based PSF Estimation for Ultrasound Training Simulation}, booktitle = {MICCAI W Simulation and Synthesis in Medical Imaging (SASHIMI)}, year = {2016}, pages = {23-33}, doi = {10.1007/978-3-319-46630-9_3} }

@inproceedings{Flach_pure_16, author = {Barbara Flach and Maxim Makhinya and Orcun Goksel}, title = {PURE: Panoramic Ultrasound Reconstruction by Seamless Stitching of Volumes}, booktitle = {MICCAI W Simulation and Synthesis in Medical Imaging (SASHIMI)}, year = {2016}, pages = {75-84}, doi = {10.1007/978-3-319-46630-9_8} }

@inproceedings{Thoma_automatic_16, author = {Janine Thoma and Firat Ozdemir and Orcun Goksel}, title = {Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker}, booktitle = {MICCAI W Medical Computer Vision (MCV)}, year = {2016}, pages = {83-93}, doi = {10.1007/978-3-319-61188-4_8} }

@inproceedings{Sanabria_non-linear_16, author = {Sergio J Sanabria and Marga Rominger and Corin F. Otesteanu and Edoardo Mazza and Orcun Goksel}, title = {Non-Linear Characterization of the Liver by Combining Shear and Longitudinal Wave Speed With Strain Observations}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2016} }

@inproceedings{Ozkan_compliance_16, author = {Ece Ozkan and Orcun Goksel}, title = {Compliance Boundary Conditions for Elasticity Reconstruction Using FEM Inverse Problem}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2016} }

@inproceedings{Otesteanu_low-cost_16, author = {Corin F. Otesteanu and Sergio J Sanabria and Orcun Goksel}, title = {Low Cost Alternative for Ultrasound Harmonic Elastography}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2016} }

* Best Paper Award
@inproceedings{Kurth_mobile_16, author = {Andreas Kurth and Andreas Tretter and Pascal A. Hager and Sergio Sanabria and Orcun Goksel and Lothar Thiele and Luca Benini}, title = {Mobile Ultrasound Imaging on Heterogeneous Multi-Core Platform}, booktitle = {ACM/IEEE Symp on Embedded Systems for Real-time Multimedia (ESTIMedia)}, year = {2016}, pages = {9-18}, doi = {10.1145/2993452.2993565} }

@inproceedings{Ozkan_inverse_16, author = {Ece Ozkan and Orcun Goksel}, title = {Inverse Problem of Ultrasound Beamforming with Sparsity in Time and Frequency Domain}, booktitle = {IEEE Int Ultrasonics Symp (IUS)}, year = {2016}, doi = {10.1109/ULTSYM.2016.7728892} }

@inproceedings{Mattausch_monte-carlo_16, author = {Oliver Mattausch and Orcun Goksel}, title = {Monte-Carlo Ray-Tracing for Realistic Interactive Ultrasound Simulation}, booktitle = {Eurographics W Visual Computing for Biology and Medicine (VCBM)}, year = {2016}, pages = {1-9}, doi = {dx.doi.org/10.2312/vcbm.20161285} }

@inproceedings{Chen_temporal_16, author = {Chen, Xiaoran and Tanner, Christine and G\"oksel, Or\ccun and Sz\'ekely, G\'abor and Luca, Valeria}, title = {Temporal Prediction of Respiratory Motion Using a Trained Ensemble of Forecasting Methods}, booktitle = {Medical Imaging and Augmented Reality (MIAR)}, year = {2016}, pages = {383-391}, doi = {10.1007/978-3-319-43775-0_35} }

@inproceedings{Otesteanu_analysis_16, author = {Corin Felix Otesteanu and Sergio J Sanabria and Orcun Goksel}, title = {Analysis of Excitation Frequency in Elasticity Reconstruction Using the FEM Inverse-Problem}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2016}, pages = {485-8}, doi = {10.1109/ISBI.2016.7493313} }

@inproceedings{Goksel_imaging_16, author = {Orcun Goksel and Valery Vishnevsky and Alvaro Gomariz Carrillo and Christine Tanner}, title = {Imaging of Sliding Visceral Interfaces During Breathing}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2016}, pages = {298-301}, doi = {10.1109/ISBI.2016.7493268} }

@inproceedings{Flach_model-based_16, author = {Barbara Flach and Maxim Makhinya and Orcun Goksel}, title = {Model-based Compensation of Tissue Deformation During Data Acquisition for Interpolative Ultrasound Simulation}, booktitle = {IEEE Int Symp Biomed Imaging (ISBI)}, year = {2016}, pages = {502-5}, doi = {10.1109/ISBI.2016.7493317} }
2015 and Earlier

@inproceedings{Makhinya_real-time_15, author = {Maxim Makhinya and Orcun Goksel}, title = {Real-time Tracking of Liver Landmarks in 2D Ultrasound Sequences}, booktitle = {4D Treatment Planning (4DTP) Workshop}, year = {2015} }

body motion, such as from breathing, which could be mitigated, if
tracked accurately in real-time. By extending an algorithm for superficial
vein tracking, we hereby present a robust real-time motion tracking
method for 2D ultrasound image sequences of the liver. The method
leverages elliptic and template-based models of vessels in the liver,
coupled with a robust optic-flow framework. Potential drifts in this
iterative tracking are corrected when the breathing phase is close
to that of the initial reference frame, detected by comparing the
appearance of tracked feature regions. Results are evaluated on the
CLUST-2015 dataset, with 1.09mm mean and 2.42mm 95th percentile errors
in 24 2D test sequences collected from four different centers.
@inproceedings{Makhinya_motion_15, author = {Maxim Makhinya and Orcun Goksel}, title = {Motion Tracking in 2D Ultrasound Using Vessel Models and Robust Optic-Flow}, booktitle = {MICCAI Challenge on Liver Motion Tracking (CLUST)}, year = {2015}, url = {http://clust.ethz.ch/opendownload/CLUST2015/makhinya_clust15.pdf} }

@inproceedings{Sanabria_hand-held_15, author = {Sergio J. Sanabria and Corin F. Otesteanu and Orcun Goksel}, title = {Hand-held Sound-speed Imaging for the Reconstruction of Bulk Modulus and Poisson Ratio in a Commercial Tissue-Mimicking Phantom}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2015} }

@inproceedings{Sanabria_total-variation_15, author = {Sergio J. Sanabria and Orcun Goksel}, title = {Total-Variation Regularization of Hand-held Limited-Angle Sound-Speed Tomography Based on Coherent Reflector for Breast Cancer Detection}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2015} }

a relatively small region of interest due either to computational
concerns or to the fact that only a part of anatomy could be observed
in the input medical images. Thus, for deformable planning or training
simulations, boundary conditions at the borders of such models are
necessitated. Zero-displacement or -force constraints at outer boundaries
are commonly used, with the assumption that the selected region is
large enough to minimize effects on the deformable behavior inside
the region of interest. This may, however, still result in errors
and does require superfluous elements to extend models. In this work,
a mixed boundary condition type called compliance boundary condition,
is proposed to constrain model boundaries. Different techniques to
define and estimate such constraints are studied in simulation experiments.
Results are presented in 2D and 3D numerical phantoms and a male
pelvic anatomical model.
@inproceedings{Ozkan_compliance_15, author = {Ece Ozkan and Orcun Goksel}, title = {Compliance Boundary Conditions for Simulating Deformations in a Limited Region of Interest}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2015}, pages = {929-32}, doi = {10.1109/EMBC.2015.7318515} }

of sonographers. A realistic appearance of simulated ultrasonic speckle
is essential for a plausible ultrasound simulation. An efficient
and realistic model for ultrasonic speckle is the convolution of
the ultrasound point-spread function with a parametrized distribution
of point scatterers. Nevertheless, for a given arbitrary tissue,
such scatterer distributions that would generate a realistic image
are not known a priori, and currently there is no principled method
to extract such scatterer patterns for given target tissues to be
simulated. In this paper we propose to solve the inverse problem,
in which an underlying scatterer map for a given sample ultrasound
image is estimated. From such scatterer maps, it is also shown that
a parametrization distribution model can be built, using which other
instances of the same tissue can be simulated by feeding into a standard
speckle generation method. This enables us to synthesize images of
different tissue types from actual ultrasound images to be used in
simulations with arbitrary view angles and transducer settings. We
show in numerical and physical tissue-mimicking phantoms and actual
physical tissue that the appearance of the synthesized images closely
match the real images.
@inproceedings{Mattausch_scatterer_15, author = {Oliver Mattausch and Orcun Goksel}, title = {Scatterer Reconstruction and Parametrization of Homogeneous Tissue for Ultrasound Image Simulation}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2015}, pages = {6350-3}, doi = {10.1109/EMBC.2015.7319845} }

@inproceedings{Baki_thermal_15t, author = {Peter Baki and Sergio J. Sanabria and Gabor Kosa and Gabor Szekely and Orcun Goksel}, title = {Thermal Expansion Imaging for Monitoring Lesion Depth using M-Mode Ultrasound during Cardiac RF Ablation: In-vitro Study}, booktitle = {Information Processing in Computer-Assisted Interventions (IPCAI)}, year = {2015} }

@inproceedings{Vishnevsky_unsupervised_15, author = {Valery Vishnevsky and Tobias Gass and Gabor Szekely and Christine Tanner and Orcun Goksel}, title = {Unsupervised Detection of Local Errors in Image Registration}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2015}, pages = {841-4}, doi = {10.1109/ISBI.2015.7164002} }

@inproceedings{Gass_multi-atlas_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Multi-Atlas Segmentation and Landmark Localization in Images with Large Fields of View}, booktitle = {MICCAI W Medical Computer Vision (MCV): Algorithms for Big Data}, year = {2014}, pages = {171-80}, doi = {10.1007/978-3-319-13972-2_16} }

* Best Poster Award at the 4D Treatment Planning Workshop in London, UK (Nov, 2014)
@inproceedings{Vishnevskiy_total_14, author = {Valeriy Vishnevskiy and Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Total Variation Regularization of Displacements in Parametric Image Registration}, booktitle = {MICCAI W Abdominal Imaging: Computational and Clinical Apps}, year = {2014}, pages = {211-220}, doi = {10.1007/978-3-319-13692-9_20} }

finding correspondences reliably between images is a difficult task
since the registration problem is ill-posed and registration algorithms
are only capable of finding local optima. This makes it challenging
to find a suitable registration method and parametrization for a
specific application. To alleviate such problems, multiple registrations
can be fused which is typically done by weighted averaging, which
is sensitive to outliers and can not guarantee that registrations
improve. In contrast, in this work we present a Markov random field
based technique which fuses registrations by explicitly minimizing
local dissimilarities of deformed source and target image, while
penalizing non-smooth deformations. We additionally propose a registration
propagation technique which combines multiple registration hypotheses
which are obtained from different indirect paths in a set of mutually
registered images. Our fused registrations are experimentally shown
to improve pair-wise correspondences in terms of average deformation
error (ADE) and target registration error (TRE) as well as improving
post-registration segmentation overlap.
@inproceedings{Gass_registration_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Registration Fusion using Markov Random Fields}, booktitle = {Workshop on Biomedical Image Registration (WBIR)}, year = {2014}, pages = {213-222}, doi = {10.1007/978-3-319-08554-8_22} }

@inproceedings{Gass_consistent_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Consistent Dense Correspondences from Pairwise Registrations}, booktitle = {SHAPE Symp on Statistical Shape Models and Applications}, year = {2014}, pages = {10} }

and landmark detection. We focus on modality and anatomy independent
techniques to be applied to a wide range of input images, in contrast
to methods customized to a specific anatomy or image modality. For
segmentation, we use label propagation from several atlases to a
target image via a Markov random field (MRF) based registration method,
followed by label fusion by majority voting weighted by local cross-correlations.
For landmark localization, we use a consensus based fusion of location
estimates from several atlases identified by a template-matching
approach. Results in IEEE ISBI 2014 VISCERAL challenge as well as
VISCERAL Anatomy1 challenge are presented herein.
@inproceedings{Goksel_segmentation_14, author = {Orcun Goksel and Tobias Gass and Gabor Szekely}, title = {Segmentation and Landmark Localization Based on Multiple Atlases}, booktitle = {IEEE ISBI VISCERAL Challenge}, year = {2014}, number = {1194}, pages = {37-43}, url = {http://ceur-ws.org/Vol-1194/visceralISBI14-5.pdf} }

clinical situations, such as cardiac failure, volume overload, and
sepsis. The measurement of CVP, however, requires insertion of a
catheter through a vein up a vena cava - close to the heart - with
related cost and risk of complications. Peripheral venous pressure
(PVP) measurement is a technique which allows indirect assessment
of CVP without catheterization. However, PVP measurement is cumbersome
since it requires several devices, trained medical personnel, and
is difficult to perform repeatably. Aiming at an automatic venous
pressure measurement system via image-processing, we introduce in
this paper a robust vessel tracking algorithm fit for this purpose.
The proposed algorithm addresses the challenge of tracking compressed
vessels, which is essential for this venous pressure measurement
technique. Given this tracking algorithm, initial PVP measurements
on healthy volunteers are reported.
@inproceedings{Crimi_vessel_14, author = {Alessandro Crimi and Maxim Makhinya and Ulrich Baumann and Gabor Szekely and Orcun Goksel}, title = {Vessel tracking for Ultrasound-based venous pressure measurement}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2014}, pages = {306-9}, doi = {10.1109/ISBI.2014.6867870} }

and correct inconsistency-based errors in non-rigid registration.
While deformable registration is ubiquitous in medical image computing,
assessing its quality has yet been an open problem. We propose a
method that predicts local registration errors of existing pairwise
registrations between a set of images, while simultaneously estimating
corrected registrations. In the solution the error is constrained
to be small in areas of high post-registration image similarity,
while local registrations are constrained to be consistent between
direct and indirect registration paths. The latter is a critical
property of an ideal registration process, and has been frequently
used to asses the performance of registration algorithms. In our
work, the consistency is used as a target criterion, for which we
efficiently find a solution using a linear least-squares model on
a coarse grid of registration control points. We show experimentally
that the local errors estimated by our algorithm correlate strongly
with true registration errors in experiments with known, dense ground-truth
deformations. Additionally, the estimated corrected registrations
consistently improve over the initial registrations in terms of average
deformation error or TRE for different registration algorithms on
both simulated and clinical data, independent of modality (MRI/CT),
dimensionality (2D/3D) and employed primary registration method (demons/Markov-randomfield).
@inproceedings{Gass_detection_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Detection and correction of inconsistency-based errors in non-rigid registration}, booktitle = {SPIE Medical Imaging}, year = {2014}, pages = {90341B}, doi = {10.1117/12.2042757} }

introducing auxiliary labels for anatomy that has similar appearance
to the target anatomy while not being part of that target. Such auxiliary
labels help avoid false positive labelling of non-target anatomy
by resolving ambiguity. A known registration of a segmented atlas
can help identify where a target segmentation should lie. Conversely,
segmentations of anatomy in two images can help them be better registered.
Joint segmentation and registration is then a method that can leverage
information from both registration and segmentation to help one another.
It has received increasing attention recently in the literature.
Often, merely a single organ of interest is labelled in the atlas.
In the presense of other anatomical structures with similar appearance,
this leads to ambiguity in intensity based segmentation; for example,
when segmenting individual bones in CT images where other bones share
the same intensity profile. To alleviate this problem, we introduce
automatic generation of additional labels in atlas segmentations,
by marking similar-appearance non-target anatomy with an auxiliary
label. Information from the auxiliary-labeled atlas segmentation
is then incorporated by using a novel coherence potential, which
penalizes differences between the deformed atlas segmentation and
the target segmentation estimate. We validated this on a joint segmentation-registration
approach that iteratively alternates between registering an atlas
and segmenting the target image to find a final anatomical segmentation.
The results show that automatic auxiliary labelling outperforms the
same approach using a single label atlasses, for both mandibular
bone segmentation in 3D-CT and corpus callosum segmentation in 2D-MRI.
@inproceedings{Gass_auxiliary_14, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Auxiliary anatomical labels for joint segmentation and atlas registration}, booktitle = {SPIE Medical Imaging}, year = {2014}, pages = {90343T}, doi = {10.1117/12.2042876} }

@inproceedings{Goksel_improving_13, author = {Or\c{c}un G\"oksel and Gabor Sz\'ekely}, title = {Improving FEM Inverse Problem Reconstructions By Incorporating All Displacement Observations Using Element Shape Function Interpolations}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2013}, pages = {78}, url = {http://www.elasticityconference.org/PDF/2013/2013ITECProceedings.pdf} }

@inproceedings{Baki_thermal_13, author = {P\'eter Baki and Gabor Sz\'ekely and Or\c{c}un G\"oksel}, title = {Thermal Expansion Imaging For Real-time Lesion Depth Assessment During RF Catheter Ablation}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2013}, pages = {70}, url = {http://www.elasticityconference.org/PDF/2013/2013ITECProceedings.pdf} }

approach for Cranio-Maxillofacial (CMF) soft tissue simulation by
considering a new image-based meshing approach that accurately models
the interface between different tissue types. The proposed approach
has been initially evaluated on soft tissue deformations of four
patients undergoing CMF surgery using post-operative CT scans. The
results indicate improved prediction and robustness of the surgical
planning outcome when compared to the state-of-the-art method while
decreasing the simulation time.
@inproceedings{Shahim_accuracy_13, author = {Kamal Shahim and Orcun Goksel and Philipp J\"urgens and Mauricio Reyes}, title = {Accuracy Improvement In Cranio-Maxillofacial Soft Tissue Simulation Using A Muscle Embedded Meshing Approach}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2013}, pages = {7156-9}, doi = {10.1109/EMBC.2013.6611208} }

@inproceedings{Ma_supervised_13, author = {Hua Ma and Thomas Coradi and Gabor Szekely and Benjamin Haas and Orcun Goksel}, title = {Supervised Learning with Global Features for Image Retrieval in Atlas-Based Segmentation of Thoracic CT}, booktitle = {Int Congress on Computer Assisted Radiology and Surgery (CARS)}, year = {2013}, pages = {S302} }

image analysis. Devising estimates of such segmentation outcome has
been of interest in cases with multiple atlases, both for single-atlas
selection and for multi-atlas fusion. This paper studies the estimation
of expected Dice's similarity metric for registering atlas-target
pairs, by employing registration loops with models of such metric
(error) accumulation over these loops. In this framework, the use
of registration information also from unsegmented images is proposed
and is shown to outperform using segmented atlas images alone. We
demonstrate a fast, memory-efficient implementation and single-atlas
selection results using a CT and an MR dataset.
@inproceedings{Goksel_estimation_13, author = {Orcun Goksel and Tobias Gass and Valery Vishnevsky and Gabor Szekely}, title = {Estimation of Atlas-Based Segmentation Outcome: Leveraging Information From Unsegmented Images}, booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)}, year = {2013}, pages = {1203-6}, doi = {10.1109/ISBI.2013.6556699} }

contexts, such as in medical training simulators. This paper presents
a methodological procedure for the creation of such virtual models
from their real-life counterparts. Both the surface geometry and
the elastic parametrization of an object are reconstructed from position/force
readings during an operator-assisted exploration of the object. A
3D mesh model is then generated from the surface contact points.
The internal elastic modulus is found using the 3D finite element
method. This modeling method is compared with two common 1D elastic
models, namely Kelvin-Voigt and Hunt-Crossley. Results using three
deformable homogeneous silicone samples show successful geometry
reconstruction. 1D model parameterizations exhibit high variation
dependent on geometry and contact location. In contrast, elastic
modulus reconstruction yields a global model parameterization independent
of geometry. Elastic moduli estimated in experiments correlated with
their known values, and were shown to be reproducible among samples
with different geometries.
@inproceedings{Goksel_deformable_13, author = {Orcun Goksel and Seokhee Jeon and Matthias Harders and Gabor Szekely}, title = {Deformable Haptic Model Generation Through Manual Exploration}, booktitle = {IEEE World Haptics Conference}, year = {2013}, pages = {543-8}, doi = {10.1109/WHC.2013.6548466} }

@inproceedings{Bolis_exploiting_12a, author = {Dimitris Bolis and Andras Jakab and Orcun Goksel and Gabor Szekely}, title = {On exploiting the connectomics for thalamic nuclei localization: application of pattern recognition techniques}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology (ESMRMB)}, year = {2012} }

* Received one of 3 best paper prizes
in this paper. Traditional atlas-based segmentation suffers from
either a strong bias towards the selected atlas or the need for manual
effort to create multiple atlas images. Similar to semi-supervised
learning in computer vision, we study a method which exploits information
contained in a set of unlabelled images by mutually registering them
non-rigidly and propagating the single atlas segmentation over multiple
such registration paths to each target. These multiple segmentation
hypotheses are then fused by local weighting based on registration
similarity. Our results on two datasets of different anatomies and
image modalities, corpus callosum MR and mandible CT images, show
a significant improvement in segmentation accuracy compared to traditional
single atlas based segmentation. We also show that the bias towards
the selected atlas is minimized using our method. Additionally, we
devise a method for the selection of intermediate targets used for
propagation, in order to reduce the number of necessary inter-target
registrations without loss of final segmentation accuracy.
@inproceedings{Gass_semi-supervised_12, author = {Tobias Gass and Gabor Szekely and Orcun Goksel}, title = {Semi-supervised Segmentation Using Multiple Segmentation Hypotheses from a Single Atlas}, booktitle = {MICCAI W Medical Computer Vision (MCV)}, year = {2012}, pages = {29-37}, doi = {10.1007/978-3-642-36620-8_4} }

@inproceedings{Bolis_exploiting_12, author = {Dimitris Bolis and Andr\'as Jakab and Or\ccun G\"oksel and G\'abor Sz\'ekely}, title = {On Exploiting Connectomics for Thalamic Nuclei Localization: A Supervised Learning Approach}, booktitle = {Int Conf on Machine Learning (ICML) Workshop on Statistics, Machine Learning and Neuroscience}, year = {2012}, url = {https://sites.google.com/site/stamlins/proceedings} }

structures are targeted by transferring a stereotactical atlas onto
the patient's anatomical images. We hypothesize that diffusion tensor
imaging and mapping of thalamocortical connections can serve as surrogate
markers of individual anatomy and can be used to predict specific
targets in the thalamus. Here we demonstrate the application of a
support vector machine (SVM) based tool that is optimized to predict
the location of the ventral intermediate nucleus. Methods and Materials:
Previously, a 3D atlas of the thalamus was non-linearly matched with
an MR template. Anatomical, diffusion tensor MR imaging and probabilistic
thalamocortical tractography to 52 cortical and subcortical areas
were performed for 40 subjects. We assumed that the volume of the
atlas-based Vim nucleus and the same structure of the subjects coincides
on standardized images in our population and can be used to train
an SVM based classifier to predict the boundaries and volume of the
Vim. Results: Using thalamocortical connectivity distributions and
the distance from the anterior commissure as features, the classifier
was able to reproduce the atlas-based location with 84 % sensitivity
and 74 % specificity. The resulting maps were able to reproduce the
gross borders of the Vim. Conclusion: We have generated patient specific
maps that showed the possible boundaries of the Vim nucleus, this
tool can be evaluated for neurosurgical targeting. We demonstrated
the applicability of this method in cases when purely atlas-based
methods might be insufficient, when the anatomy is disrupted (a tumor
case) or unknown (pediatric cases).
@inproceedings{Bolis_application_12, author = {Dimitris Bolis and Andras Jakab and Orcun Goksel and Gabor Szekely}, title = {Application of pattern recognition techniques to locate the Vim thalamic nucleus based on thalamocortical tractography}, booktitle = {European Congress on Radiology (ECR)}, year = {2012}, pages = {C-1991} }

(MR) images of the prostate can aid diagnosis and treatment planning
for prostate cancer. Surface segmentations of the prostate are available
in both modalities. Our goal is to develop a 3D deformable registration
method based on these segmentations and a biomechanical model. The
segmented source volume is meshed and a linear finite element model
is created for it. This volume is deformed to the target image volume
by applying surface forces computed by assuming a negative relative
pressure between the non-overlapping regions of the volumes and the
overlapping ones. This pressure drives the model to increase the
volume overlap until the surfaces are aligned. We tested our algorithm
on prostate surfaces extracted from post-operative MR and TRUS images
for 14 patients, using a model with elasticity parameters in the
range reported in the literature for the prostate. We used three
evaluation metrics for validating our technique: the Dice Similarity
Coefficient (DSC) (ideally equal to 1.0), which is a measure of volume
alignment, the volume change in source surface during registration,
which is a measure of volume preservation, and the distance between
the urethras to assess the anatomical correctness of the method.
We obtained a DSC of 0.96+-0.02 and a mean distance between the urethras
of 1.5+-1.4 mm. The change in the volume of the source surface was
1.5+-1.4%. Our results show that this method is a promising tool for
physicallybased deformable surface registration.
@inproceedings{Taquee_deformable_12, author = {Farheen Taquee and Orcun Goksel and S. Sara Mahdavi and Mira Keyes and W. James Morris and Ingrid Spadinger and Septimiu Salcudean}, title = {Deformable Prostate Registration from MR and TRUS Images Using Surface Error Driven FEM models}, booktitle = {SPIE Medical Imaging}, year = {2012}, doi = {10.1117/12.911688} }

@inproceedings{Goksel_haptic_11n, author = {Orcun Goksel and Kirill Sapchuk and Septimiu E. Salcudean}, title = {Haptic simulation of needle and probe interaction with tissue for prostate brachytherapy training}, booktitle = {IEEE World Haptics Conference (WHC)}, year = {2011}, pages = {7-12}, doi = {10.1109/WHC.2011.5945453} }

@inproceedings{Goksel_fem_10, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {FEM Simulation of Harmonic Tissue Excitation for Prostate Elastography}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2010}, pages = {93} }

@inproceedings{Eskandari_bandpass_10, author = {Hani Eskandari and Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {Bandpass Sampling of High Frequency Tissue Motion}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2010}, pages = {71} }

@inproceedings{Goksel_mesh_10, author = {Orcun Goksel and Hani Eskandari and Septimiu E. Salcudean}, title = {Mesh Adaptation for Improving Inverse-Problem Reconstruction}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2010}, pages = {96} }

has been an active topic of research in the past decade. Although
dynamic feedback control of needle insertion systems is expected
to provide more accurate target tracking, it has received little
attention due to the fact that most available models for needle-tissue
interaction do not incorporate the dynamics of motions. In this paper,
we study the controllability of rigid or flexible needles inside
soft tissues using mechanical-based dynamic models. The results have
significant implications on the design of suitable feedback controllers
for different types of needle insertion systems.
@inproceedings{Haddadi_controllability_10, author = {Amir Haddadi and Orcun Goksel and Septimiu E. Salcudean and Keyvan Hashtrudi-Zaad}, title = {On the Controllability of Dynamic Model-Based Needle Insertion in Soft Tissue}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2010}, pages = {2287-2291}, doi = {10.1109/IEMBS.2010.5627676} }

@inproceedings{Goksel_haptic_10, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {Haptic Simulator for Prostate Brachytherapy with Simulated Ultrasound}, booktitle = {Int Symp Biomedical Simulation (ISBMS)}, year = {2010}, pages = {150-9}, doi = {10.1007/978-3-642-11615-5_16} }

@inproceedings{Zahiri-Azar_multi-dimensional_09, author = {Reza Zahiri-Azar and Orcun Goksel and T.S. Yao and Ehsan Dehghan and Joseph Yan and Septimiu E. Salcudean}, title = {Multi-Dimensional Sub-Sample Motion Estimation: Initial Results}, booktitle = {IEEE Int Ultrasonics Symposium (IUS)}, year = {2009}, doi = {10.1109/ULTSYM.2009.0595} }

* One of 8 Student Best Paper Finalists
@inproceedings{Goksel_automatic_09, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {Automatic Prostate Segmentation from Transrectal Ultrasound Elastography Images Using Geometric Active Contours}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2009}, pages = {34} }

@inproceedings{Zahiri-Azar_application_09, author = {Reza Zahiri-Azar and Orcun Goksel and Septimiu E. Salcudean}, title = {Application of 2D Polynomial Fitting to Beam Steering for Motion Estimation with Sub-Sample Accuracy}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2009}, pages = {124} }

@inproceedings{Zahiri-Azar_methods_09, author = {Reza Zahiri-Azar and Orcun Goksel and Septimiu E. Salcudean}, title = {Methods for the Estimation of the Sub-Sample Motion Using Digitized Ultrasound Echo Signals in Three Dimensions}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2009}, pages = {88} }

* Received one of 15 Hamlyn Robotics Travel Grants
@inproceedings{Goksel_high-quality_09, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {High-Quality Model Generation for Finite Element Simulation of Tissue Deformation}, booktitle = {MICCAI}, year = {2009}, pages = {248-256}, doi = {10.1007/978-3-642-04271-3_31} }

images is required for radiation treatment of prostate cancer. Manual
segmentation is a time-consuming task, the results of which are dependent
on image quality and physicians' experience. This paper introduces
a semi-automatic 3D method based on super-ellipsoidal shapes. It
produces a 3D segmentation in less than 15 seconds using a warped,
tapered ellipsoid fit to the prostate. A study of patient images
shows good performance and repeatability. This method is currently
in clinical use at the Vancouver Cancer Center where it has become
the standard segmentation procedure for low dose-rate brachytherapy
treatment.
@inproceedings{Mahdavi_3d_09, author = {Sara Mahdavi and Orcun Goksel and Septimiu E. Salcudean}, title = {3D Prostate Segmentation in Ultrasound Images Based on Tapered and Deformed Ellipsoids}, booktitle = {MICCAI}, year = {2009}, pages = {960-7}, doi = {10.1007/978-3-642-04271-3_116} }

@inproceedings{Zahiri-Azar_methods_08, author = {Reza Zahiri-Azar and Orcun Goksel and T.S. Yao and Ehsan Dehghan and Joseph Yan and Septimiu E. Salcudean}, title = {Methods for the Estimation of Sub-sample Motion of Digitized Ultrasound Echo Signals in 2D}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2008}, pages = {5581-4}, doi = {10.1109/IEMBS.2008.4650479} }

@inproceedings{Zahiri-Azar_real-time_07, author = {Reza Zahiri-Azar and Orcun Goksel and Septimiu E. Salcudean}, title = {Real-Time Tissue Deformation Visualization}, booktitle = {Int Tissue Elasticity Conf (ITEC)}, year = {2007}, pages = {117} }

@inproceedings{Goksel_real-time_07, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {Real-time Synthesis of Image Slices in Deformed Tissue from Nominal Volume Images}, booktitle = {MICCAI}, year = {2007}, pages = {401-8}, doi = {10.1007/978-3-540-75757-3_49} }

@inproceedings{Goksel_fast_07, author = {Orcun Goksel and Septimiu E. Salcudean}, title = {Fast B-Mode Ultrasound Image Simulation of Deformed Tissue}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2007}, pages = {87-90}, doi = {10.1109/IEMBS.2007.4352229} }

@inproceedings{Goksel_simulation_07, author = {Orcun Goksel and Reza Zahiri-Azar and Septimiu E. Salcudean}, title = {Simulation of Ultrasound Radio-Frequency Signals in Deformed Tissue for Validation of 2D Motion Estimation with Sub-Sample Accuracy}, booktitle = {IEEE Eng Medicine and Biology Conf (EMBC)}, year = {2007}, pages = {2159-62}, doi = {10.1109/IEMBS.2007.4352750} }

needle insertion simulation and path planning. In this paper, three
models are compared in terms of accuracy in simulating the bending
of a prostate brachytherapy needle. The first two utilize the finite
element method, one using geometric non-linearity and triangular
plane elements, the other using non-linear beam elements. The third
model uses angular springs to model cantilever deflection. The simulations
are compared with the experimental bent needle configurations. The
models are assessed in terms of geometric conformity using independently
identified and pre-identified model parameters. The results show
that the angular spring model, which is also the simplest, simulates
the needle more accurately than the others.
@inproceedings{Dehghan_comparison_06, author = {Ehsan Dehghan and Orcun Goksel and Septimiu E. Salcudean}, title = {A Comparison of Needle Bending Models}, booktitle = {MICCAI}, year = {2006}, pages = {305-312}, doi = {10.1007/11866565_38} }

* First place in the Student Paper Competition
@inproceedings{Goksel_image_06, author = {Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {Image Synthesis of Deformed Tissue with Application to Ultrasound for Prostate Brachytherapy}, booktitle = {Canadian Medical and Biological Engineering Conf (CMBEC)}, year = {2006} }

extension of a prior work based on the finite element method. The
model is also adapted to accommodate arbitrary meshes so that the
anatomy can effectively be meshed using third-party algorithms. Using
this model a prostate brachytherapy simulator is designed to help
medical residents acquire needle steering skills. This simulation
uses a prostate mesh generated from clinical data segmented as contours
on parallel slices. Node repositioning and addition, which are methods
for achieving needle-tissue coupling, are discussed. In order to
achieve real-time haptic rates, computational approaches to these
methods are compared. Specifically, the benefit of using the Woodbury
formula (matrix inversion lemma) is studied. Our simulation of needle
insertion into a prostate is shown to run faster than 1 kHz.
@inproceedings{Goksel_3d_05, author = {Orcun Goksel and Simon P. DiMaio and Septimiu E. Salcudean and Robert Rohling and James Morris}, title = {3D Needle-Tissue Interaction Simulation for Prostate Brachytherapy}, booktitle = {MICCAI}, year = {2005}, pages = {827-834}, doi = {10.1007/11566465_102} }

* Received the Best Poster Award
@inproceedings{Goksel_3Dis_05, author = {Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {3D Needle-Tissue Interaction Simulation for Prostate Brachytherapy}, booktitle = {Annual Canadian Conference on Intelligent Systems (IS 2005)}, year = {2005} }

* Received the ASI Innovation Award and Communication Awards
@inproceedings{Goksel_towards_04, author = {Orcun Goksel and Septimiu E. Salcudean and Robert Rohling}, title = {Towards a Prostate Brachytherapy Haptic Simulator}, booktitle = {BC Advanced Systems Institute (ASI) Exchange}, year = {2004} }
Monographs:

* Western Association of Graduate Schools (WAGS) Innovation in Technology Award for the top-ranked dissertation of the year for western North America
@phdthesis{Goksel_meshing_09, author = {Orcun Goksel}, title = {Meshing and Rendering of Patient-Specific Deformation Models With Application to Needle Insertion Simulation}, school = {University of British Columbia}, year = {2009}, url = {http://hdl.handle.net/2429/17418} }

@mastersthesis{Goksel_ultrasound_05, author = {Orcun Goksel}, title = {Ultrasound Image and 3D Finite Element based Tissue Deformation Simulator for Prostate Brachytherapy}, school = {University of British Columbia}, year = {2005}, url = {http://hdl.handle.net/2429/16194} }
Edited Books:

@book{Franz_ipcai_21, author = {Alfred Franz and Orcun Goksel}, title = {(eds.) IJCARS-IPCAI 2021 Special Issue: Proceedings of Information Processing for Computer-Assisted Interventions}, publisher = {Springer}, year = {2021}, doi = {10.1007/s11548-021-02401-5} }

@book{Spiclin_wbir_20, author = {Spiclin, Z. and McClelland, J. and Kybic, J. and Goksel, O}, title = {(eds.) Proceedings of International Workshop on Biomedical Image Registration (WBIR)}, publisher = {Springer}, year = {2020}, doi = {10.1007/978-3-030-50120-4} }

@book{Gooya_sashimi_18, author = {Gooya, A. and Goksel, O. and Oguz, I. and Burgos, N.}, title = {(eds.) Proceedings of Simulation and Synthesis in Medical Imaging (SASHIMI)}, publisher = {Springer}, year = {2018}, doi = {10.1007/978-3-030-00536-8} }

@book{Goksel_visceral_15, author = {Orcun Goksel and Jim\'enez del Toro, Oscar Alfonso and Antonio Foncubierta and Henning M\"uller}, title = {(eds.) Proceedings of the VISCERAL Challenge at ISBI}, publisher = {CEUR-WS.org}, year = {2015}, number = {1390}, url = {http://ceur-ws.org/Vol-1390/} }
@book{Goksel_visceral_14, author = {Orcun Goksel}, title = {(ed.) Proceedings of the VISCERAL Challenge at ISBI}, publisher = {CEUR-WS.org}, year = {2014}, number = {1194}, url = {http://ceur-ws.org/Vol-1194} }
Non-reviewed Reports (Online / ArXiv):

@unpublished{Thandiackal_match_21, author = {Kevin Thandiackal and Tiziano Portenier and Andrea Giovannini and Maria Gabrani and Orcun Goksel}, title = {Match What Matters: Generative Implicit Feature Replay for Continual Learning}, year = {2021}, number = {arxiv:2106.05350}, url = {https://arxiv.org/abs/2106.05350} }

@unpublished{Teuscher_mechanotransduction_21, author = {Alina C Teuscher and Cyril Statzer and Seraina A Domenig and Ingmar Schoen and Viola Vogel and Orcun Goksel and Collin Yv\'es Ewald}, title = {Mechanotransduction Coordinates Inter-Tissue Extracellular Matrix Protein Homeostasis Promoting Longevity in C. Elegans}, journal = {SSRN: Cell Sneak Peek}, year = {2021}, doi = {10.2139/ssrn.3881355} }

@unpublished{Khodadadi_motion_21, author = {Hossein Khodadadi and Orcun Goksel and Sabine Kling}, title = {Motion Estimation for Optical Coherence Elastography Using Signal Phase and Intensity}, year = {2021}, number = {arxiv:2103.10784}, url = {https://arxiv.org/abs/2103.10784} }

@unpublished{Ozdemir_delineating_20, author = {Firat Ozdemir and Christine Tanner and Orcun Goksel}, title = {Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation}, year = {2020}, number = {arxiv:2001.02001}, pages = {1-11}, url = {https://arxiv.org/abs/2001.02001} }

@unpublished{Tanner_generative_18, author = {Christine Tanner and Firat Ozdemir and Romy Profanter and Valeriy Vishnevsky and Ender Konukoglu and Orcun Goksel}, title = {Generative Adversarial Networks for MR-CT Deformable Image Registration}, year = {2018}, number = {arXiv:1807.07349}, pages = {1-11}, url = {https://arxiv.org/abs/1807.07349} }

@unpublished{Ozkan_herding_17, author = {Ece Ozkan and Gemma Roig and Orcun Goksel and Xavier Boix}, title = {Herding Generalizes Diverse M-Best Solutions}, year = {2016}, number = {arXiv:1611.04353}, pages = {1-8}, url = {https://arxiv.org/abs/1611.04353} }

@unpublished{Azar_strain_11, author = {Reza Zahiri Azar and Orcun Goksel and Ehsan Dehghan and Septimiu E Salcudean}, title = {Strain Tensor Visualization Using Glyph Objects: Application to Elastography}, year = {2011}, pages = {1-4}, doi = {10.13140/RG.2.2.31558.37446} }