Publications

  • Jadie Adams & Shireen Elhabian (2024). Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud. International Conference on Learning Representations (ICLR), 2024. Published, 05/2024.
  • Hong Xu & Shireen Elhabian (2024). Optimization-Driven Statistical Models of Anatomies using Radial Basis Function Shape Representation. IEEE International Symposium on Biomedical Imaging (ISBI), 2024. Published, 05/2024.
  • Tushar Kataria, Beatrice Knudsen & Shireen Elhabian (2024). Unsupervised Domain Adaptation for Semantic Segmentation via Feature-space Density Matching. IEEE International Symposium on Biomedical Imaging (ISBI), 2024. Published, 05/2024.
  • Andrew Peterson, Jason Nelson, Jacob Benna, Shireen Y. Elhabian, Cesar de Cesar Netto, Timothy Beals (2024). Charcot-Marie-Tooth Talar Morphology Analysis. Orthopaedic Research Society (abstract). Published, 02/2024.
  • Elana Lapins, Zachary Eatough, Charles Saltzman, Shireen Y. Elhabian, Amy Lenz (2024). Morpho- logical Analysis of Isolated Subtalar Osteoarthritis via 21-Bone Statistical Shape Modeling of the Foot. Orthopaedic Research Society (abstract). Published, 02/2024.
  • Bhalodia R, Elhabian S & Adams J (2023). DeepSSM: A blueprint for image-to-shape deep learning models. Medical image analysis. Vol. 91, 103034103034. Published, 12/01/2023.
  • Arefeen Sultan, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella & Shireen Elhabian (2023). Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 230-239). Cham: Springer Nature Switzerland.. Published, 10/2023.
  • Jadie Adams & Shireen Elhabian (2023). Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation. InInternational Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging 2023 Oct 7 (pp. 53-63). Cham: Springer Nature Switzerland.. Published, 10/2023.
  • Atkins PR & Morris A (2023). A Correspondence-Based Network Approach for Groupwise Analysis of Patient-Specific Spatiotemporal Data. Annals of biomedical engineering. Vol. 51, 228923002289-2300. Published, 09/01/2023.
  • Janmesh Ukey & Shireen Elhabian (2023). Localization-aware Deep Learning Framework for Statistical Shape Modeling Directly from Images. Medical Imaging with Deep Learning (MIDL). Published, 07/2023.
  • Penny R. Atkins, Alan Morris, Shireen Y. Elhabian, Andrew E. Anderson. Application Of Correspondence-based Networks To The Analysis Of Spatial And Temporal Biomechanics Data. Orthopaedic Research Society (ORS) Annual Meeting, 2023. Published, 01/2023.
  • Penny R. Atkins, Shireen Y. Elhabian, Jeffrey A. Weiss, Ross T. Whitaker, Christopher L. Peters, Andrew E. Anderson. Combination of Statistical Shape Modeling and Statistical Parametric Mapping to Quantify Cartilage Contact Mechanics in Hip Dysplasia. PanaSoMM 2022 Abstract. Published, 01/2023.
  • Penny R. Atkins, Praful Agrawal, Joseph D. Mozingo, Keisuke Uemura, Kunihiko Tokunaga, Christopher L. Peters, Shireen Y. Elhabian, Ross T. Whitaker, Andrew E. Anderson. Use of Statistical Shape Modeling to Predict Clinical Metrics of Femoral Head Coverage in Patients with Developmental Dysplasia. PanaSoMM 2022 Abstract. Published, 01/2023.
  • Penny R. Atkins, Alan Morris, Shireen Y. Elhabian, Andrew E. Anderson. Correspondence-based Statistical Analysis of Subject-specific Hip Biomechanics. The 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE), 2023. Published, 01/2023.
  • Surojit Saha, Shireen Elhabian, and Ross Whitaker. GENs: Generative Encoding Networks. Machine Learning, Springer, 2022. Published, 11/2022.
  • Jadie Adams, Nawazish Khan, Alan Morris, Shireen Y. Elhabian. Spatiotemporal Cardiac Sta- tistical Shape Modeling: A Data-Driven Approach. MICCAI-STACOM 2022. (arXiv:2209.02736). Published, 09/2022.
  • Krithika Iyer, Alan Morris, Brian Zenger, Karthik Karanth, Oleksandre Korshak, Shireen Y. Elhabian. Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries. MICCAI- STACOM 2022. (arXiv:2209.02706). Published, 09/2022.
  • Krithika Iyer, Riddhish Bhalodia, Shireen Y. Elhabian. RENs: Relevance Encoding Networks. arXiv:2205.13061, 2022. Published, 05/2022.
  • Goparaju A, Iyer K, Bône A, Hu N, Henninger HB & Anderson AE (2022). Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Medical image analysis. Vol. 76, 102271102271. Published, 04/01/2022.
  • Penny R. Atkins, Shireen Y. Elhabian, Jeffrey A. Weiss, Ross T. Whitaker, Christopher L. Peters, Andrew E. Anderson. Quantification of Cartilage Mechanics through Statistical Shape Modeling and Statistical Parametric Mapping. Orthopedics Research Society (ORS) Annual Meeting, 2022. Published, 03/2022.
  • Penny R. Atkins, Praful Agrawal, Joseph D. Mozingo, Keisuke Uemura, Kunihiko Tokunaga, Christopher L. Peters, Shireen Y. Elhabian, Ross T. Whitaker, Andrew E. Anderson. Prediction of Clinical Measures of Femoral Head Coverage from Statistical Shape Modeling Parameters in Patients with Developmental Dysplasia. Orthopedics Research Society (podium presentation), 2022. Published, 03/2022.
  • Andrew C. Peterson, Rich J. Lisonbee, Nicola Krahenbuhl, Charles L. Saltzman, Andrew E. Anderson, Alexej Barg, Shireen Elhabian, Amy L. Lenz. Multi-Domain Statistical Shape Model of the Subtalar, Talonavicular and Calcaneocuboid Joints. Orthopedics Research Society, 2022. Published, 03/2022.
  • Nawazish Khan, Andrew C. Peterson, Benjamin Aubert, Alan Morris, Penny R. Atkins, Amy L. Lenz, Andrew E. Anderson, Shireen Y. Elhabian. Statistical Multi-Level Shape Models for Scalable Modeling of Multi-Organ Anatomies. Frontiers in Bioengineering and Biotechnology. 2023. Published, 02/2022.
  • Penny R. Atkins & Praful Agrawal, Joseph D. Mozingo, Keisuke Uemura, Kunihiko Tokunaga, Christopher L. Peters, Shireen Y. Elhabian, Ross T. Whitaker, Andrew E. Anderson (2022). Prediction of Clinical Measures of Femoral Head Coverage from Statistical Shape Modeling Parameters in Patients with Developmental Dysplasia. Orthopedics Research Society. Published, 02/2022.
  • Penny R. Atkins & Shireen Y. Elhabian, Jeffrey A. Weiss, Ross T. Whitaker, Christopher L. Peters, Andrew E. Anderson (2022). Quantification of Cartilage Mechanics through Statistical Shape Modeling and Statistical Parametric Mapping. Orthopedics Research Society. Published, 02/2022.
  • Wenzheng Tao, Riddhish Bhalodia, Shireen Y. Elhabian. Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model. arXiv:2201.03481, 2022. Published, 01/2022.
  • Agrawal P & Mozingo JD (2021). Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.. (pp. 111-121). Vol. 12474, Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Published, 10/01/2021.
  • Riddhish Bhalodia, Shireen Y. Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross T. Whitaker. DeepSSM: A Blueprint for Image to Shape Deep Learning Models. arXiv preprint arXiv:2110.07152, 2021. Published, 10/2021.
  • Adams J (2021). Uncertain-DeepSSM: From Images to Probabilistic Shape Models.. (pp. 57-72). Vol. 12474, Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Published, 10/01/2021.
  • Morgan AE, Kashani A, Zenger B, Rupp LC, Perez MD & Foote MD (2021). Right Ventricular Shape Distortion in Tricuspid Regurgitation. Computing in cardiology. Vol. 47. Published, 09/01/2021.
  • Bhalodia R & Elhabian S (2021). Leveraging unsupervised image registration for discovery of landmark shape descriptor. Medical image analysis. Vol. 73, 102157. Published, 09/01/2021.
  • Joseph D. Mozingo, Penny R. Atkins, Praful Agrawal, Keisuke Uemura, Shireen Y. Elhabian, Ross T. Whitaker, Andrew E. Anderson. Morphology of Hip Dysplasia in Japanese Females: A Statistical Shape Modeling Study. American Society of Biomechanics (ASB) Conference, 2021. Published, 08/2021.
  • Joseph D. Mozingo & Penny R. Atkins, Praful Agrawal, Keisuke Uemura, Shireen Y. Elhabian, Ross T. Whitaker, Andrew E. Anderson (2021). Morphology of Hip Dysplasia in Japanese Females: A Statistical Shape Modeling Study. American Society of Biomechanics (ASB) Conference. Published, 08/2021.
  • Alessandro Ferrero & Shireen Y. Elhabian, Ross T. Whitaker (2021). SetGAN: Improving the Stability and Diversity of Generative Models through a Permutation Invariant Architecture. The 25th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV),. Published, 07/2021.
  • Praful Agrawal, Ross Whitaker, Shireen Y. Elhabian. Learning Deep Features for Shape Correspondence with Domain Invariance. arXiv preprint arXiv:2102.10493, 2021. Published, 02/2021.
  • Daniel Perry & Vahid Keshavarzzadeh,Shireen Elhabian, Robert Kirby, Michael Gleicher, Ross Whitaker. (2020). isualization of topology optimization designs with representative subset selection. arXiv. Published, 12/2020.
  • Daniel Perry, Vahid Keshavarzzadeh, Shireen Elhabian, Robert Kirby, Michael Gleicher, Ross Whitaker. Visualization of topology optimization designs with representative subset selection. arXiv preprint arXiv:2012.14901. December 2020. Published, 12/2020.
  • Jacxsens M, Elhabian SY, Brady SE, Chalmers PN, Mueller AM, Tashjian RZ & Henninger HB (2020). Thinking outside the glenohumeral box: Hierarchical shape variation of the periarticular anatomy of the scapula using statistical shape modeling. Journal of orthopaedic research : official publication of the Orthopaedic Research Society. Vol. 38, 2272-2279. Published, 12/01/2020.
  • Surojit Saha & Shireen Elhabian, Ross Whitaker (2020). GENs: Generative Encoding Networks. arXiv. Published, 10/2020.
  • Anupama Goparaju & Alexandre Bone, Nan Hu, Heath B. Henninger, Andrew E. Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs, Nassir Marrouche, Shireen Y. Elhabian (2020). Benchmarking Off-the-Shelf Statistical Shape Modeling Tools in Clinical Applications. arXiv. Published, 09/2020.
  • Alan Morris & ugene Kholmovski, Nassir Marrouche, Joshua Cates, Shireen Y. Elhabian (2020). AnImage-based Approach for 3D Left Atrium Functional Measurements. Computing in Cardiology. Published, 09/2020.
  • Agrawal P, Whitaker RT & Elhabian SY (2020). An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps. IEEE transactions on medical imaging. Vol. 39, 2316-2326. Published, 08/01/2020.
  • Wei Xing & Shireen Y. Elhabian, Vahid Keshavarzzadeh, Robert M. Kirby (2020). Shared-GP: Learning Interpretable Shared Hidden Structure Across Data Spaces for Design Space Analysis and Exploration. ASME Journal of Mechanical Design. Published, 01/2020.
  • Jacxsens M, Elhabian SY, Brady SE, Chalmers PN, Tashjian RZ & Henninger HB (2019). Coracoacromial morphology: a contributor to recurrent traumatic anterior glenohumeral instability?. Journal of shoulder and elbow surgery. Vol. 28, 1316-1325.e1. Published, 11/01/2019.
  • Sultana S, Agrawal P, Elhabian S, Whitaker R, Blatt JE, Gilles B, Cetas J, Rashid T & Audette MA (2019). Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models. International journal of computer assisted radiology and surgery. Vol. 14, 1955-1967. Published, 11/01/2019.
  • Shireen Elhabian & Riddhish Bhalodia, Archanasri Subramanian, Alan, Morris, Joshua Cates, Ross Whitaker, Evgueni Kholmovski, Nassir Marrouche, Shireen Y. Elhabian (2019). Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?. Computing in Cardiology (CinC). Published, 09/2019.
  • Shireen Elhabian & Anupama Goparaju, Alan Morris, Ibolya Csecs, Riddhish Bhalodia, Tom Ditter, Kristine Fuimaono, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Shireen Y. Elhabian (2019). Interatrial Septum and Appendage Ostium in Atrial Fibrillation Patients: A Population Study. Computing in Cardiology (CinC). Published, 09/2019.
  • Shireen Elhabian & Shalin Parikh, Anupama Goparaju, Riddhish Bhalodia, Bosten Loveless, Alan Morris, Joshua Cates, Eugueni Kholmovski, Nassir Marrouche, Shireen Y. Elhabian. (2019). Efficient Segmentation Pipeline using Diffeomorphic Image Registration: A Validation Study. Computing in Cardiology (CinC). Published, 09/2019.
  • Heath B. Henninger & Matthijs Jacxsens, Shireen Y. Elhabian, Peter N. Chalmers, Robert Z. Tashjian, Heath B. Henninger. (2019). Thinking Outside the Glenohumeral Box: Hierarchical Shape Variation of the Periarticular Anatomy of the Scapula Using Statistical Shape Modeling. Anatomy & Biomechanics at International Congress of Shoulder and Elbow Therapist (ICSES). Published, 09/2019.
  • Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan, Ross Whitaker. CoopSubNet: Coop- erating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets. arXiv:1906.05441, 2019. Published, 06/2019.
  • Sultana Sharmin & Praful Agrawal,Shireen Y. Elhabian, Ross Whitaker, Jason E. Blatt, BenjaminGilles, Justin Cetas, Tanweer Rashid, Michel A. Audette (2019). Medial Axis Segmentation of CranialNerves Using Shape Statistics-Aware Discrete Deformable Model. International Journal ofComputer Assisted Radiology and Surger. Published, 06/2019.
  • Ross Whitaker & Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan, Ross Whitaker. (2019). CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets. arXiv. Published, 04/2019.
  • Ross Whitaker & Alessandro Ferrero, Shireen Y. Elhabian, Ross Whitaker (2019). SetGANs: Enforcing Distributional Accuracy in Generative Adversarial Networks. arXiv. Published, 04/2019.
  • Atkins P. & Shin Y., Agrawal P., Elhabian S., Whitaker R., Weiss J., Aoki S., Peters C., Anderson A. (2019). Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?. Clinical Orthopaedics and Related Research. Vol. 477, 242-253. Published, 01/01/2019.
  • Bhalodia R, Elhabian SY, Kavan L & Whitaker RT (2018). DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images.. (pp. 244-257). Vol. 11167, Shape in Medical Imaging : International Workshop, ShapeMI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. International Workshop on Shape in Medical Imaging (2018 : Granada, Spain). Published, 11/01/2018.
  • Elhabian S. & Sodergren S., Goparaju A., Morris A., Kholmovski E., Marrouche N., Cates J., Elhabian S. (2018). Interactive Exploration of Left Atrium Population-Level Morphology in Atrial Fibrillation Patients. Computation in Cardiology (CinC). Published, 09/23/2018.
  • Elhabian S. & Bhalodia R., Goparaju A., Morris A., Kholmovski E., Marrouche N., Cates J., Whitaker R., Elhabian S. (2018). Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation. Computation in Cardiology (CinC). Published, 09/23/2018.
  • Elhabian S. & Sodergren T., Bhalodia R., Whitaker R., Cates J., Marrouche N., Elhabian S. (2018). Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation. STACOM-MICCAI: Statistical Atlases and Computational Modeling of the Heart workshop. Published, 09/2018.
  • Elhabian S. & Goparaju A., Csecs I., Morris A., Kholmovski E., Marrouche N., Whitaker R., Elhabian S. (2018). On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application.. In MICCAI: International Workshop on Shape in Medical Imaging, Springer, Cham.. Published, 09/2018.
  • Ross Whitaker & Yen Yun Yu, Shireen Y. Elhabian, Ross T. Whitaker. (2018). Clustering with Pairwise Relationships: A Generative Approach. arXiv. Published, 05/2018.
  • Matthijs Jacxsens, Shireen Y. Elhabian, Peter N. Chalmers, Robert Z. Tashjian, Heath B. Henninger. Periarticular and Glenoid Morphology of the Scapula Differs in Patients with Hill- Sachs Lesions: A Controlled Statistical Shape Modeling Study. Orthopedics Research Society (ORS), 2018. Published, 03/2018.
  • Jacxsens M., Elhabian S., Chalmers P., Tashjian R., Henninger H. (2018). Periarticular and Glenoid Morphology of the Scapula Differs in Patients with Hill-Sachs Lesions: a Controlled Statistical Shape Modeling Study. Orthopedics Research Society (ORS). Published, 03/2018.
  • Tu L, Styner M, Vicory J, Elhabian S, Wang R, Hong J, Paniagua B, Prieto JC, Yang D, Whitaker R & Pizer SM (2018). Skeletal Shape Correspondence Through Entropy. IEEE transactions on medical imaging. Vol. 37, 1-11. Published, 01/01/2018.
  • Atkins, P.R., Elhabian, S.Y., Agrawal, P., Harris, M.D., Whitaker, R.T., Weiss, J.A., Peters, C.L. and Anderson, A.E., (2017). Quantitative comparison of cortical bone thickness using correspondence‐based shape modeling in patients with cam femoroacetabular impingement. Journal of Orthopaedic Research, 35(8), pp.1743-1753. Published, 09/01/2017.
  • Jacxsens M., Elhabian S., Tashjian R., Henninger H. (2017). Scapular Morphology In Patients With Hill-Sachs Lesions Using Statistical Shape Modeling. The 27th Congress of the European Society for Surgery of the Shoulder and the Elbow (SECEC-ESSSE) conference, 2017. Published, 09/2017.
  • Veni G, Elhabian SY & Whitaker RT (2017). ShapeCut: Bayesian surface estimation using shape-driven graph. Medical image analysis. Vol. 40, 11-29. Published, 08/01/2017.
  • Elhabian, S. and Whitaker, R., (2017). ShapeOdds: Variational Bayesian Learning of Generative Shape Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2231-2242). Published, 07/2017.
  • Atkins P., Aoki S., Elhabian S., Agrawal P., Whitaker R., Weiss J., Peters C., Anderson A.. Evaluation of the Sclerotic Subchondral Bone Boundary as a Surgical Resection Guide in the Treatment of Cam-type Femoroacetabular Impingement. Annual Meeting of Orthopaedic Research Society, 2017. Published, 03/2017.
  • Anderson, A., Atkins, P.R., Agrawal, P., Elhabian, S.Y., Whitaker, R.T., Weiss, J.A., Peters, C.L. and Aoki, S.K., (2016). Which Radiographic Measurements Best Identify Anatomical Variation in Femoral Head Anatomy? Analysis Using 3D Computed Tomography and Statistical Shape Modeling. Journal of hip preservation surgery, 3(suppl_1). Published, 05/2016.
  • Elhabian, S.Y., Agrawal, P. and Whitaker, R.T., (2016). Optimal parameter map estimation for shape representation: A generative approach. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 660-663). IEEE. Published, 04/2016.
  • Elhabian, S., Gur, Y., Vachet, C., Piven, J., Styner, M., Leppert, I., Pike, G.B. and Gerig, G., (2014). A preliminary study on the effect of motion correction on HARDI reconstruction. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on (pp. 1055-1058). IEEE. Published, 04/2014.
  • Farag, A., Elhabian, S., Graham, J., Farag, A. and Falk, R., (2010). Toward precise pulmonary nodule descriptors for nodule type classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 626-633). Springer, Berlin, Heidelberg. Published, 09/2010.
  • Farag, A., Elhabian, S., Graham, J., Farag, A., Elshazly, S., Falk, R., Mahdi, H., Abdelmunim, H. and Al-Ghaafary, S., (2010). Modeling of the lung nodules for detection in LDCT scans. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE (pp. 3618-3621). IEEE. Published, 08/2010.

Research Keywords

  • Statistical Analysis
  • Pattern Recognition
  • Medical Image Processing
  • Machine Learning/ Artifical Intelligence
  • Deep Learning
  • Computer Vision
  • Biomedical and biological image processing

Presentations

  • Data-driven Shape Analysis: Methods, Applications, and Future. Data Science Seminar Series, University of Utah, November 2022. Invited Talk/Keynote, Presented, 11/2022.
  • When Machine Learning Meets Shape Analysis: Transforming Clinical Evaluation of Anatomies. The Scientific Computing and Imaging Institute, University of Utah, May 2022. Other, Presented, 05/2022.
  • Transforming Clinical Evaluation of Anatomies: Theory, Tools, and Applications. The University of Utah Research Seminar Series at the Cardiovascular Research and Training Institute (CVRTI). Invited Talk/Keynote, Presented, 10/2021.
  • Anatomy Representation and Analysis with ShapeWorks. The 17th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE). Other, Presented, 09/2021.
  • Statistical Shape Modeling & ShapeWorks: Present and Future. The Summer Biomechan- ics, Bioengineering, and Biotransport Conference (SB3C). Other, Presented, 06/2021.
  • ShapeWorks in Python, ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 12/2020.
  • ShapeWorks in Python. ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah . Other, Presented, 12/2020.
  • ShapeWorks APIs, ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 07/2020.
  • ShapeWorks APIs. ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 07/2020.
  • Shape Modeling and Analysis with ShapeWorks, ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 09/2019.
  • Shape Modeling and Analysis with ShapeWorks. ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 09/2019.
  • Introduction to Shape Analysis. ShapeWorks Users Get-Together Series. The Scientific Computing and Imaging Institute, University of Utah. Other, Presented, 08/2019.
  • The summer course on Image-based Biomedical Modeling (IBBM). Title: Shape Analysis. Invited Talk/Keynote, Presented, 07/2018.
  • The summer course on Image-based Biomedical Modeling (IBBM). Title: Geometric Transformations and Image Registration. Invited Talk/Keynote, Presented, 07/2018.
  • Shireen Y. Elhabian and Ross T. Whitaker. ShapeOdds: Variational Bayesian Learning of Generative Shape Models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2231-2242, 2017. Conference Paper, Refereed, Presented, 07/23/2017.
  • The summer course on Image-based Biomedical Modeling (IBBM). Title: “ShapeWorksStudio v2.2 Particle-based Shape Correspondence and Visualization Software”, July 2017 . Invited Talk/Keynote, Presented, 07/2017.
  • Shireen Y. Elhabian and Ross T. Whitaker. From Label Maps to Generative Shape Models: A Variational Bayesian Learning Approach. In International Conference on Information Processing in Medical Imaging (IPMI), pp. 93-105, 2017. Conference Paper, Refereed, Presented, 06/28/2017.
  • Imaging lunch seminar series at the Scientific Computing and Imaging Institute, Unversity of Utah. Title: “ShapeOdds: Variational Bayesian Learning of Generative Shape Models”. Invited Talk/Keynote, Presented, 03/2017.
  • Data group seminar series at the School of Computing, University of Utah. Title: “From Silhouettes to Generative Shape Models: A Variational Bayesian Learning Approach”. Invited Talk/Keynote, Presented, 02/2017.

Research Groups

  • Md Hasibul Husain Hisham, Graduate Student. Kahlert School of Computing. 08/21/2023 - present.
  • Jarom Hogue, Postdoc. Scientific Computing and Imaging Institute. 04/01/2023 - present.
  • Shikha Dubey, Postdoc. Scientific Computing and Imaging Institute. 01/16/2023 - present.
  • Jake Wagoner, Other. 11/28/2022 - present.
  • K M Arefeen Sultan, Graduate Student. School of Computing. 08/16/2022 - present.
  • Rachaell Nihalaani, Graduate Student. School of Computing. 08/16/2022 - present.
  • Janmesh Ukey, Graduate Student. School of Computing. 08/16/2022 - present.
  • Mokshagna Karanam, Graduate Student. School of Computing. 08/16/2022 - present.
  • Abu Zhid Bin Aziz, Graduate Student. School of Computing. 08/16/2022 - present.

Languages

  • Arabic, fluent.
  • English, fluent.

Geographical Regions of Interest

  • Asia
  • Europe
  • Northern Africa
  • Northern America

Software Titles

  • ShapeWorks 6.4.0. What is new? ShapeWorks Back-end New shapeworks 'analyze' command for offline analysis of shape models Improved free form constraints that now support doubly connected areas (e.g. donut-like) Data Portal migrated to new ShapeWorks Cloud and swcc (ShapeWorks Cloud Client) tool ShapeWorks can now be used as a 3rd party library (details here) Added support for 'save_init_splits' and 'checkpointing_interval' parameters in the project spreadsheet formats Added new JSON based file format for storing ShapeWorks projects (swproj) Added a unified logging library for ShapeWorks (spdlog) Uniform numerical computation of specificity across platforms Updated dependencies. Python now at 3.9, PyTorch 1.11.0, VTK 9.1, ITK 5.2.1 ShapeWorks Front-end Studio can now automatically check for updates and prompt the user to download them Improved progress bar in Studio is more representative and also estimates time remaining Added ability to arbitrarily scale difference arrows in Studio Added file association support for swproj file extension on Windows and Mac New multi-level analysis feature in Studio (details here) Added ability to hide/show particles per domain User's Support Hip Use Case: The use case uses the hip joint to demonstrate the capability of ShapeWorks to capture inter-domain correlations and interactions directly on triangular surface meshes. The use case showcases calculating the alignment options available for multiple organ anatomies. (details here) Added options to the incremental use case when run in --interactive mode, including sorting method, initial model size, and incremental batch size. The sorting method determines how the shapes are sorted to be added incrementally. There are three options: random, median, distribution. (details here) Python API documentation has been added to the ShapeWorks documentation site . Release Date: 05/2023. Inventors: Elhabian Research Group.
  • ShapeWorks v6.3.2. Fix install_shapeworks.sh on Linux. Release Date: 10/2022.
  • ShapeWorks 6.3.1. Fix Studio python problem causing crash on group differences. Fix Studio slow/hanging on certain free form constraints. Fix crashes on certain projects. Release Date: 07/2022.
  • ShapeWorks 6.3.0. ShapeWorks Back-end: Added constraints functionality for the mesh domain both clipping and augmented lagrangian together with a flag to flip between the two options. ShapeWorks Front-end: Added new support for showing the difference to the mean for any given mesh (subjects or generated PCA mode positions). PCA Montage and Fringe plot export. Image volume support: New support has been added for displaying 2D slices from image volumes (e.g. CT/MRI). Landmark placement UI in Studio, landmark registration, landmarks as initializers. Cutting planes UI in Studio: Added new support for defining and manipulating cutting planes. Free form constraints UI in Studio: Added new support for defining free form constraints. Group LDA chart in Studio: Support for the group LDA chart has been added in Studio. User's Support: Added grooming steps to mesh-based use cases using the mesh Python API. Alignment transforms are now passed to the optimizer and used in optimization instead of being applied before optimization. This results in local particles in the original data's coordinate system, allowing for easier subsequent analysis. The use cases now use project spreadsheets in optimizations instead of XML files. This format is more interpretable and allows of better integration with Studio. The project sheets support multiple domains, fixed domains, and constraints. DeepSSM Use Case: The DeepSSM use case has been updated to demonstrate the full pipeline, including training data. generation instead of relying on the femur use case to create a training shape model. Image-to-image registration tools have been added to prepare DeepSSM input images without requiring corresponding segmentations or meshes. This allows for true inference with DeepSSM. Release Date: 06/2022.
  • ShapeWorks 6.2.1. A minor release that includes new features in the backend and frontend. Release Date: 01/2022. Inventors: Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Krithika Iyer.
  • ShapeWorks 6.2 . A major release that includes new mesh grooming tools, supported for free form constraints, multiple domains in Studio, new analysis and usability features in Studio, and deep learning support in Studio. Release Date: 11/2021. Inventors: Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Krithika Iyer.
  • ShapeWorks 6.1. A major release that includes the support for convoluted structures and contours, mesh grooming support in Studio, and new use cases. Release Date: 06/2021. Inventors: Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Krithika Iyer, Karthik Karanth.
  • ShapeWorks 6.0. A new major release that includes a new discussion forum, tiny tests for use cases, running use cases on subsets, generating shape cohorts, demonstration notebooks, consolidation of mesh-based grooming tools, mesh support in Studio, new and faster surface reconstruction, feature maps support for meshes, detailed optimization progress, and deep learning use case. Release Date: 03/2021. Inventors: Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Karthik Karanth, Oleks Korshak.
  • ShapeWorks v5.5 . A new major release for ShapeWorks that includes revamped documentation, optimized shape models for use cases, optimizing shape models on meshes, new ShapeWorks API, feature maps support, new interface for Studio, and deep learning use cases. Release Date: 10/14/2020. Inventors: Shireen Elhabian, ShapeWorks development team (Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Karthik Karanth, Oleks Korshak).
  • ShapeWorks v5.4. A new major release for ShapeWorks that includes a new flexible project file format, an improved back-end with lower memory footprint and faster optimization, automated development builds, and a scalable user interface. Release Date: 06/12/2020. Inventors: Shireen Elhabian, ShapeWorks development team (Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Karthik Karanth, Oleks Korshak, Atefeh Ghanaatikashani).
  • ShapeWorks v5.3. A new major release for ShapeWorks that includes use cases, an improved user interface, and a flexible and cross platform build process. Release Date: 02/25/2020. Inventors: Shireen Elhabian, ShapeWorks development team (Alan Morris, Cameron Christensen, Archanasri Subramanian, Riddhish Bhalodia, Jadie Adams, Hong Xu, Karthik Karanth, Oleks Korshak, Atefeh Ghanaatikashani).
  • ShapeWorks5.2.1. ShapeWorks is a free, open-source suite of software tools that uses a flexible method for automated construction of compact statistical landmark-based shape models of ensembles of anatomical shapes that do not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. ShapeWorks includes tools for preprocessing data, computing landmark-based shape models, and visualizing the results. Release Date: 11/2019. Inventors: Shireen Elhabian, Ross Whitaker.
  • ShapeWorks (latest release). ShapeWorks is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. Release Date: 07/2018. Inventors: Ross Whitaker, Joshua Cates, Shireen Elhabian.
  • ShapeWorksStudio. The ShapeWorksStudio software is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. The proposed correspondence point optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. The ShapeWorks software includes tools for preprocessing data, computing point-based shape models, and visualizing the results. Release Date: 07/2017. Inventors: Ross Whitaker, Joshua Cates, Shireen Elhabian.