ShapeMI

We gladly announce the workshop on Shape in Medical Imaging (ShapeMI), 27th of September (8.00 - 12.30) in the Berlin (G) room, which is held in conjunction with the conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2026) in Strasbourg, France. This workshop is the sixth instance of ShapeMI, after successful ShapeMI'18, ShapeMI'20, ShapeMI'23, ShapeMI'24, and ShapeMI'25.

This workshop aims to present leading methods and applications for advanced shape analysis and geometric learning in medical imaging. It will provide a venue for researchers working in shape and geometric modeling, learning, analysis, statistics, classification, and applications to share novel ideas, present recent research results, and interact with each other. Today’s medical image data typically represent three-dimensional geometric structures and dynamic, time-varying anatomical processes. Shape and geometry processing methods continue to play a crucial role because of their sensitivity to subtle morphological variations. Data-driven differential geometry for shape and spectral analysis and modeling remains a central focus of this workshop. At the same time, rapid advances in deep learning research are reshaping how the mathematical foundations of computational anatomy are used. A new theme of ShapeMI 2026 is how shape is being integrated with modern AI architectures. Recent progress in transformer-based point cloud models, equivariant neural networks, neural fields, and large geometric foundation models is reshaping how core concepts of computational anatomy are operationalized through applied research in healthcare. We will look forward to receiving methodology or applied research in topics that emphasize how classical shape theory informs the design, interpretability, and generalization of modern geometric deep learning models. Furthermore, anatomical shape does not exist in isolation, and biomedical research that only includes shape will generate discoveries that will remain siloed within the structural realm. This workshop will place special emphasis on multi-modal and multi-scale shape-driven biomarkers, including the fusion of shape descriptors with multiomics data, clinical and electronic health records, longitudinal disease trajectories, and biomechanical simulations. This focus reflects a growing shift from shape analysis as a standalone methodology toward shape-informed, integrative modeling pipelines for precision medicine and population-level inference.

Posters and pitch videos

Posters and 1-minute pitch videos for the 2026 papers, as received. Browse all files: posters · pitch videos. Entries marked — have not reached us yet and are added as they arrive.

Oral presentations (slides are not published here)

#	Title	Authors
#3	AutoFFS: Adversarial Deformations for Facial Feminization Surgery Planning	Paul Friedrich, Florentin Bieder, Florian M. Thieringer et al.
#12	SPVR: Explicit Shape-Prior-Guided 3D Vertebral Reconstruction from Orthogonal Projections	Marijn de Lange, Jianing Wang, Han Liu et al.
#18	Deep Shape Regression for Planar Curves with Multimodal Covariates	Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin et al.
#19	ZODIAC: Zero-shot Octree-based Diffusion for Anatomical Completion	Miruna-Alexandra Gafencu, Vlad Bratulescu, Yordanka Velikova et al.
#27	Learning the Marfan Face: A Conditional Mesh VAE for Explainable Screening and Synthetic Generation	Giuseppe Maurizio Facchi, Raffaella Lanzarotti, Giuliano Grossi et al.
#47	SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warping	Nawazish Khan, Sanjay Bhandari, Sarang Joshi et al.

Posters

#	Title	Authors	Material
#4	Exploring the Influence of Prenatal Alcohol Exposure on Face Shape and Brain Development	Daniel Zieger, Yana Kuznetcov, Maximilian Weiherer et al.	Poster · 1-min pitch
#6	Landmark Detection Supported by Anatomy Segmentation and Geometric Constraints	Loris Giordano, Sebastian Amador Sanchez, Tom Lenaerts et al.	Poster · 1-min pitch
#7	Learning to Reconnect: Graph-based Path Classification For Restoring Retinal Vessel Segmentation Connectivity	Oscar Morand, Nathan Painchaud, Jonathan Fabrizio et al.	Poster · 1-min pitch
#9	On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling	Asma Khan, Tushar Kataria, Janmesh Ukey et al.	Poster · 1-min pitch
#10	Spherical RePaint: Label-Reusable Diffusion-Based Augmentation for Parcellation	Kangmin Kim, Jongmin Kim, Jiwon Son et al.	—
#13	Mind the Gap: Mesh-Guided Repair of Broken Vessels	Gniewosz Drwiega, Wojciech Szymanski, Marek Wodzinski	Poster · 1-min pitch
#14	A Joint 2D–3D Statistical Shape Model for Orthopedic Reconstruction	Florence Dell'Aniello Picard, Pranav Poudel, Nairouz Shehata et al.	Poster · 1-min pitch
#15	VessComNet: Geometry-Aware 3D Vessel Completion Across Non-Contrast and Contrast-Enhanced CT Images	Linlin Yao, Vivek Batheja, Praveen Thoppey Srinivasan Balamuralikrishna et al.	Poster · 1-min pitch
#16	What Does Anatomical Shape Know About You? A Multi-Organ, Shape-Only Study of Demographic Prediction, Attribution, and Privacy	Gijs Luijten, Behrus Hinrichs-Puladi, Merlin Engelke et al.	Poster · 1-min pitch · 3-min
#17	Predicting The Progression of Adolescent Idiopathic Scoliosis	Owen Pullen, Amir Jamaludin, Andrew Zisserman	Poster · 1-min pitch
#20	Segmentation and keypoints transfer for volumetric medical shapes via functional maps	Francesca Maccarone, Filippo Maggioli, Denis Peruzzo et al.	Poster · 1-min pitch
#21	PanVasc: A Shape-Aware Framework for Peripancreatic Vascular Invasion Assessment in Pancreatic Ductal Adenocarcinoma	Muhammad Ibtsaam Qadir, Alexa J. Hughes, Ryan J. Ellis et al.	Poster
#22	Extending the Evolutionary Skeletal Representation to the Cerebral Cortex Anatomy	David Allemang, Jared Vicory, Stephen Pizer	Poster · 1-min pitch
#25	BlendNet: on the Importance of Accurate Local Pre-Registration in the Image Registration Task	Gabriel Ravelomanana, Julien Pansiot, Sergi Pujades	Poster · 1-min pitch
#26	Medial Skeletons for Haustral Fold Detection in Colonoscopy	Samuel Ehrenstein, Sarah McGill, Julian Rosenman et al.	—
#28	Disentangled Geometric Variational Learning of Normative Pediatric Development from Anatomical Surface Representations of Patients with Pathology	Joseph Nagel, Ines Alejandro Cruz Guerrero, Brooke French et al.	Poster · 1-min pitch
#31	Cardiac Shape Priors for Equivariant Neural Field Representations of Cine MRI	Sacha Buijs, Fleur Valerie Yuen Tjong, Erik J Bekkers et al.	Poster · 1-min pitch
#33	TRACE: Artifact-Robust Statistical Shape Modeling from Imperfect Surface Scans - A Case Study in Craniosynostosis 3D Photography	Sanjay Bhandari, Nawazish Khan, Alzbeta Novotna et al.	Poster · 1-min pitch
#35	DINE: Distance Is Not Enough Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration	Sara Monji-Azad, Rohit Beer, Marvin Kinz et al.	—
#36	Interpretable Obstructive Sleep Apnea Screening from MRI-Derived Upper-Airway Surface Morphology with Class Node Graph Attention Networks	Aryan Mokhtari Saghafi, Jay Devine, Kaat Pauwels et al.	Poster · 1-min pitch
#37	KneeFlow: A Flow Matching Framework for Patient-Specific Knee Reconstruction	Ragavan Srinivasan, Vivek maik, Manojkumar Lakshmanan et al.	—
#38	Multiscale Wave-Shaping Neural Encoding with Cross-Attention Networks for 3D Organ Reconstruction and Generation	Diego Alejandro Flores Menjivar, Panagiotis Kalozoumis, Dimitris Iakovidis	Poster · 1-min pitch
#40	Rethinking Sequence Modeling on Cortical Surfaces: Robustness, Spatial Ordering, and Resolution in Infant Brain Age Prediction	Hye Jin Yoon, Jin Kyu Gahm	Poster · 1-min pitch
#41	An Implicit 3D Face and Palate Shape Model for Infant Orofacial Clefts	Till N. Schnabel, Yoriko Lill, Maximilian Weiherer et al.	Poster · 1-min pitch · 3-min
#42	NO2SSM: A Discretization-Invariant Neural-Operator Approach to Statistical Shape Modeling	Abu Zahid Bin Aziz, Shireen Elhabian	Poster · 1-min pitch
#43	Learning To Focus: Anatomy-Guided Attention Regularization for Medical Image Classification	Tonmoy Hossain, Md. Atiqur Rahman, Farhana Hossain Swarnali et al.	—
#44	Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddings	Hao Ding, Daniel Semchin, Paul Thompson et al.	Poster · 1-min pitch
#45	Machine learning on subcortical brain features: A study of sample size efficiency for neurodegenerative disease classification	Yanghee Im, Melody J.Y. Kang, Boris Gutman et al.	Poster · 1-min pitch
#46	Mixture of Experts for Fine-Grained Shape Refinement in Medical Segmentation	Denisa Checiu, Giacomo Grazia, Arshana Ramautar et al.	Poster · 1-min pitch

2025 Proceedings

https://link.springer.com/book/10.1007/978-3-032-06774-6

2024 Proceedings

https://link.springer.com/book/10.1007/978-3-031-75291-9

2023 Proceedings

https://link.springer.com/book/10.1007/978-3-031-46914-5

Best paper award winners 2026 🏆

Topics

This workshop targets theoretical contributions as well as exciting applications in medical imaging, including (but not limited to):

Shape Processing and Analysis
Shape Learning and Classification
Geometric Learning and Manifold-based Methods
Statistics of Shapes and Deformations
Geometry-constrained Deep Learning and Optimization
Synthetic Anatomical Shape Generation
Generative Shape Models
Spectral Shape Analysis
Spectral Clustering and Dimensionality Reduction
Shape Modeling and Representation
Shape Segmentation, Registration and Correspondence
Longitudinal Shape Analysis and Processing
Multimodal AI integration using geometric features
Large geometric foundation models
Medical Applications Focused on Shape Analysis
Evaluation / Quality Assessment of Shape Models
Relevant Demos of Freely Available Shape Analysis Software

Academic objectives

The workshop will call for paper submissions on three different topics, or combinations thereof: methodology, applications, and software platforms in shape modeling and statistics. The best papers will be presented in oral sessions. A poster session will host the remaining accepted papers and will provide ample opportunity for in-depth discussion of all submitted topics. As in previous years, we will encourage presenters to showcase any software platform that resulted from the presented work. As a single-track workshop, ShapeMI will feature excellent keynote speakers, technical paper presentations and demonstrations of state-of-the-art software for shape processing in medical research.

Data (optional)

If you are looking for medical shapes for your work, take a look at MedShapeNet, which is a large-scale dataset of 3D medical shapes.

Organizers

Christian Wachinger, Technical University of Munich, Munich, Germany
Beatriz Paniagua, Kitware Inc, Carrboro, US and University of North Carolina in Chapel Hill, Chapel Hill, US
Gijs Luijten, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Shireen Elhabian, School of Computing, Scientific Computing and Imaging Institute, University of Utah, USA
Karthik Gopinath, Harvard Medical School, Massachusetts General Hospital, USA
Jan Egger, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany

Advisory Board / Program Committee

As in previous years, we will have a highly qualified advisory board, similar to ShapeMI 2018, 2020, 2023, 2024, and 2025, as listed below.

Kumar Abhishek, Simon Fraser University, Burnaby, Canada
Suyash Awate, Indian Institute of Technology Bombay, Mumbai, India
Devkumar Biswas, Indian Institute of Technology Ropar, Rupnagar, India
Satrajit Chakrabarty, GE HealthCare, San Ramon, United States
Tim Cootes, University of Manchester, Manchester, United Kingdom
Saher Elsayed, University of Pennsylvania, Philadelphia, United States
André Ferreira, University of Minho, Braga, Portugal
Ellen Gasparovic, Union College, Schenectady, United States
Palak Handa, Danube Private University, Krems, Austria
Tonmoy Hossain, Harvard Medical School & Massachusetts General Hospital, Boston, United States
Krithika Iyer, Children's National Hospital, Washington, D.C., United States
Jens Kleesiek, UK Essen, Essen, Germany
Chaitanya Kolluru, Elucid Bioimaging, Boston, United States
Kathryn Leonard, Occidental College, Los Angeles, United States
Hao Li, Vanderbilt University, Nashville, United States
Diana Mateus, École Centrale de Nantes, Nantes, France
Kaveh Moradkhani, École de technologie supérieure, Montreal, Canada
Krishna Mridha, Case Western Reserve University, Cleveland, United States
Mohammad Mehedi Hasan Munna, East West University, Dhaka, Bangladesh
Marc Niethammer, University of California San Diego, San Diego, United States
Ilkay Oksuz, Istanbul Technical University, Istanbul, Turkey
Steve Pizer, University of North Carolina at Chapel Hill, Chapel Hill, United States
Behrus Hinrichs-Puladi, RWTH Aachen University, Aachen, Germany
Md Mostafijur Rahman, University of Texas at Austin, Austin, United States
Mira Rizkallah, Laboratoire des Sciences du Numérique de Nantes, Nantes, France
Farnaz Sheikhi, University of Calgary, Calgary, Canada
Stefan Sommer, University of Copenhagen, Copenhagen, Denmark
Martin Styner, University of Basel, Basel, Switzerland & University of North Carolina, Chapel Hill, USA
Matthias Wilms, University of Michigan, Ann Arbor, United States