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arxiv: 2308.16139 · v5 · pith:FDDE7P3Onew · submitted 2023-08-30 · 💻 cs.CV · cs.DB· cs.LG

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

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

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