mlsanityData to 14 Oct 2026

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

arXiv 2308.16139 ↗First posted 30 Aug 2023cs.CV cs.DB cs.LGcited by 32

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Abstract (arXiv)

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedback

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 77 of its 215 references that are in the map. First posted on arXiv 30 Aug 2023.

Builds on

77 of 215 references.

References · 77

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38 more
  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 15; 2026: 9.

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  1. 2025CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answeringcited by 21
  2. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  3. 2023SADIR: Shape-Aware Diffusion Models for 3D Image Reconstructioncited by 12
  4. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  5. 2023Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstructioncited by 4
  6. 2024Label Critic: Design Data Before Modelscited by 4
  7. 2026Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunitiescited by 4
  8. 2025See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancementcited by 3
  9. 2024GAInS: Gradient Anomaly-aware Biomedical Instance Segmentationcited by 2
  10. 2024GERA: Geometric Embedding for Efficient Point Registration Analysiscited by 2
  11. 2025Hierarchical Feature Learning for Medical Point Clouds via State Space Modelcited by 2
  12. 2025A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variationcited by 2
20 more
  1. 2025From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Reviewcited by 2
  2. 2026GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Taskscited by 2
  3. 2024Thermodynamics-informed graph neural networks for real-time simulation of digital human twinscited by 1
  4. 2025Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representationscited by 1
  5. 2025Memorization in 3D Shape Generation: An Empirical Studycited by 1
  6. 2026High-Fidelity Medical Shape Generation via Skeletal Latent Diffusioncited by 1
  7. 2024Mean-field Chaos Diffusion Modelscited by 0
  8. 2024UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registrationcited by 0
  9. 2025Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstructioncited by 0
  10. 2025Shap-MeDcited by 0
  11. 2025Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functionscited by 0
  12. 2025NFR: Neural Feature-Guided Non-Rigid Shape Registrationcited by 0
  13. 2025ShapeKitcited by 0
  14. 2025Benchmark-Ready 3D Anatomical Shape Classificationcited by 0
  15. 2026Leveraging a Statistical Shape Model for Efficient Generation of Annotated Training Data: A Case Study on Liver Landmarks Segmentationcited by 0
  16. 2026Training-free Detection and 6D Pose Estimation of Unseen Surgical Instrumentscited by 0
  17. 2026Structured Analytic Coherent Point Drift for Non-Rigid Point Set Registrationcited by 0
  18. 2026Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completioncited by 0
  19. 2026Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Headcited by 0
  20. 2026Mind the Gap: Mesh-Guided Repair of Broken Vesselscited by 0