Abstract
We present MedShapeNet, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D surgical instrument models. Prior to the deep learning era, the broad application of statistical shape models (SSMs) in medical image analysis is evidence that shapes have been commonly used to describe medical data. Nowadays, however, state-of-the-art (SOTA) deep learning algorithms in medical imaging are predominantly voxel-based. In computer vision, on the contrary, shapes (including, voxel occupancy grids, meshes, point clouds and implicit surface models) are preferred data representations in 3D, as seen from the numerous shape-related publications in premier vision conferences, such as the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), as well as the increasing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models) in computer vision research. MedShapeNet is created as an alternative to these commonly used shape benchmarks to facilitate the translation of data-driven vision algorithms to medical applications, and it extends the opportunities to adapt SOTA vision algorithms to solve critical medical problems. Besides, the majority of the medical shapes in MedShapeNet are modeled directly on the imaging data of real patients, and therefore it complements well existing shape benchmarks comprising of computer-aided design (CAD) models. MedShapeNet currently includes more than 100,000 medical shapes, and provides annotations in the form of paired data. It is therefore also a freely available repository of 3D models for extended reality (virtual reality - VR, augmented reality - AR, mixed reality - MR) and medical 3D printing. This white paper describes in detail the motivations behind MedShapeNet, the shape acquisition procedures, the use cases, as well as the usage of the online shape search portal: https://medshapenet.ikim.nrw/
Jianning Li
Postdoc
Gijs Luijten
PhD Student
Enrico Nasca
Research Engineer
Anjany Sekuboyina
Postdoc
Amin Dada
PhD Student
Miriam Balzer
PhD Student
Jana Fragemann
PhD Student
Frederic Jonske
PhD Student
Moritz Rempe
PhD Student
Constantin Seibold
Team Lead Computer Vision
Michael Kamp
Team Lead Trustworthy Machine Learning
Amr Abourayya
PhD Student
Alexander Brehmer
PhD Student
Lukas Heine
PhD Student
Julius Keyl
Medical Doctor
Moon Kim
Team Lead Medical Informatics
Björn Menze
Professor for Biomedical Image Analysis and Machine Learning
Jens Kleesiek
Professor of Translational Image-guided Oncology
