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

30 August 2023
Jianning Li
Antonio Pepe
Jiancheng Yang
Antonio Pepe
Christina Schwarz-Gsaxner
Gijs Luijten
Chongyu Qu
Tiezheng Zhang
Xiaoxi Chen
Wenxuan Li
Marek Wodzinski
Paul Friedrich
Ezequiel de la Rosa
Yuan Jin
Narmada Ambigapathy
Enrico Nasca
Naida Solak
G. Melito
Viet Duc Vu
A. R. Memon
Christopher Schlachta
Sandrine De Ribaupierre
Rajnikant Patel
Roy Eagleson
Xiaojun Chen
Heinrich Mächler
Hans Liebl
Ezequiel de la Rosa
Patrich Ferndinand Christ
Hongwei Bran Li
David G. Ellis
M. Aizenberg
S. Gatidis
T. Kuestner
N. Shusharina
N. Heller
Vincent Andrearczyk
Adrien Depeursinge
M. Hatt
Anjany Sekuboyina
Maximilian Loeffler
Hans Liebl
Reuben Dorent
Tom Vercauteren
J. Shapey
Aaron Kujawa
S. Cornelissen
P. Langenhuizen
A. Ben-Hamadou
Ahmed Rekik
S. Pujades
Edmond Boyer
Federico Bolelli
C. Grana
Luca Lumetti
H. Salehi
Jun Ma
Yao Zhang
R. Gharleghi
S. Beier
Arcot Sowmya
E. Garza-Villarreal
T. Balducci
Diego Angeles-Valdez
R. Souza
Letícia Rittner
Richard Frayne
Yuanfeng Ji
Vincenzo Ferrari
S. Chatterjee
Florian Dubost
Stefanie Schreiber
Hendrik Mattern
Oliver Speck
Daniel Haehn
Christoph John
A. Nuernberger
J. Pedrosa
Carlos A. Ferreira
Guilherme Aresta
António Cunha
A. Campilho
Yannick R Suter
Jose A Garcia
A. Lalande
Vicky Vandenbossche
Aline Van Oevelen
Kate Duquesne
Hamza Mekhzoum
Jef Vandemeulebroucke
E. Audenaert
C. Krebs
T. V. Leeuwen
E. Vereecke
Hauke Heidemeyer
R. Roehrig
F. Hoelzle
V. Badeli
Kathrin Krieger
Matthias Gunzer
Jianxu Chen
Timo van Meegdenburg
Amin Dada
M. Balzer
Jana Fragemann
F. Jonske
Moritz Rempe
Stanislav Malorodov
F. Bahnsen
C. Seibold
A. Jaus
Zdravko Marinov
Paul F. Jaeger
Rainer Stiefelhagen
A. Santos
    MedIm3DH
ArXiv (abs)PDFHTMLGithub (78★)
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/

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