MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision
Authors: Li Jianning , Zhou Zongwei , Yang Jiancheng , Pepe Antonio , Gsaxner Christina , Luijten Gijs , Qu Chongyu , Zhang Tiezheng , Chen Xiaoxi , Li Wenxuan , Wodzinski Marek , Friedrich Paul , Xie Kangxian , Jin Yuan , Ambigapathy Narmada , Nasca Enrico , Solak Naida , Melito Gian Marco , Vu Viet Duc , Memon Afaque R. , Schlachta Christopher , De Ribaupierre Sandrine , Patel Rajnikant , Eagleson Roy , Chen Xiaojun , Mächler Heinrich , Kirschke Jan Stefan , de la Rosa Ezequiel , Christ Patrick Ferdinand , Li Hongwei Bran , Ellis David G. , Aizenberg Michele R. , Gatidis Sergios , Küstner Thomas , Shusharina Nadya , Heller Nicholas , Andrearczyk Vincent , Depeursinge Adrien , Hatt Mathieu , Sekuboyina Anjany , Löffler Maximilian T. , Liebl Hans , Dorent Reuben , Vercauteren Tom , Shapey Jonathan , Kujawa Aaron , Cornelissen Stefan , Langenhuizen Patrick , Ben-Hamadou Achraf , Rekik Ahmed , Pujades Sergi , Boyer Edmond , Bolelli Federico , Grana Costantino , Lumetti Luca , Salehi Hamidreza , Ma Jun , Zhang Yao , Gharleghi Ramtin , Beier Susann , Sowmya Arcot , Garza-Villarreal Eduardo A. , Balducci Thania , Angeles-Valdez Diego , Souza Roberto , Rittner Leticia , Frayne Richard , Ji Yuanfeng , Ferrari Vincenzo , Chatterjee Soumick , Dubost Florian , Schreiber Stefanie , Mattern Hendrik , Speck Oliver , Haehn Daniel , John Christoph , Nürnberger Andreas , Pedrosa João , Ferreira Carlos , Aresta Guilherme , Cunha António , Campilho Aurélio , Suter Yannick , Garcia Jose , Lalande Alain , Vandenbossche Vicky , Van Oevelen Aline , Duquesne Kate , Mekhzoum Hamza , Vandemeulebroucke Jef , Audenaert Emmanuel , Krebs Claudia , van Leeuwen Timo , Vereecke Evie , Heidemeyer Hauke , Röhrig Rainer , Hölzle Frank , Badeli Vahid , Krieger Kathrin , Gunzer Matthias , Chen Jianxu , van Meegdenburg Timo , Dada Amin , Balzer Miriam , Fragemann Jana , Jonske Frederic , Rempe Moritz , Malorodov Stanislav , Bahnsen Fin H. , Seibold Constantin , Jaus Alexander , Marinov Zdravko , Jaeger Paul F. , Stiefelhagen Rainer , Santos Ana Sofia , Lindo Mariana , Ferreira André , Alves Victor , Kamp Michael , Abourayya Amr , Nensa Felix , Hörst Fabian , Brehmer Alexander , Heine Lukas , Hanusrichter Yannik , Weßling Martin , Dudda Marcel , Podleska Lars E. , Fink Matthias A. , Keyl Julius , Tserpes Konstantinos , Kim Moon-Sung , Elhabian Shireen , Lamecker Hans , Zukić Dženan , Paniagua Beatriz , Wachinger Christian , Urschler Martin , Duong Luc , Wasserthal Jakob , Hoyer Peter F. , Basu Oliver , Maal Thomas , Witjes Max J. H. , Schiele Gregor , Chang Ti-chiun , Ahmadi Seyed-Ahmad , Luo Ping , Menze Bjoern , Reyes Mauricio , Deserno Thomas M. , Davatzikos Christos , Puladi Behrus , Fua Pascal , Yuille Alan L. , Kleesiek Jens , Egger Jan 
Published: December 2024

Objectives

The shape is commonly used to describe the objects. State-of-the-art 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 the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing.

Methods

We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing.

Results

By now, MedShapeNet includes 23 datasets 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 and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing.

Conclusions

MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.