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@ARTICLE{Li:276343,
author = {Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and
Pepe, Antonio and Gsaxner, Christina and Luijten, Gijs and
Qu, Chongyu and Zhang, Tiezheng and Chen, Xiaoxi and Li,
Wenxuan and Wodzinski, Marek and Friedrich, Paul and Xie,
Kangxian and Jin, Yuan and Ambigapathy, Narmada and Nasca,
Enrico and Solak, Naida and Melito, Gian Marco and Vu, Viet
Duc and Memon, Afaque R and Schlachta, Christopher and De
Ribaupierre, Sandrine and Patel, Rajnikant and Eagleson, Roy
and Chen, Xiaojun and Mächler, Heinrich and Kirschke, Jan
Stefan and de la Rosa, Ezequiel and Christ, Patrick
Ferdinand and Li, Hongwei Bran and Ellis, David G and
Aizenberg, Michele R and Gatidis, Sergios and Küstner,
Thomas and Shusharina, Nadya and Heller, Nicholas and
Andrearczyk, Vincent and Depeursinge, Adrien and Hatt,
Mathieu and Sekuboyina, Anjany and Löffler, Maximilian T
and Liebl, Hans and Dorent, Reuben and Vercauteren, Tom and
Shapey, Jonathan and Kujawa, Aaron and Cornelissen, Stefan
and Langenhuizen, Patrick and Ben-Hamadou, Achraf and Rekik,
Ahmed and Pujades, Sergi and Boyer, Edmond and Bolelli,
Federico and Grana, Costantino and Lumetti, Luca and Salehi,
Hamidreza and Ma, Jun and Zhang, Yao and Gharleghi, Ramtin
and Beier, Susann and Sowmya, Arcot and Garza-Villarreal,
Eduardo A and Balducci, Thania and Angeles-Valdez, Diego and
Souza, Roberto and Rittner, Leticia and Frayne, Richard and
Ji, Yuanfeng and Ferrari, Vincenzo and Chatterjee, Soumick
and Dubost, Florian and Schreiber, Stefanie and Mattern,
Hendrik and Speck, Oliver and Haehn, Daniel and John,
Christoph and Nürnberger, Andreas and Pedrosa, João and
Ferreira, Carlos and Aresta, Guilherme and Cunha, António
and Campilho, Aurélio and Suter, Yannick and Garcia, Jose
and Lalande, Alain and Vandenbossche, Vicky and Van Oevelen,
Aline and Duquesne, Kate and Mekhzoum, Hamza and
Vandemeulebroucke, Jef and Audenaert, Emmanuel and Krebs,
Claudia and van Leeuwen, Timo and Vereecke, Evie and
Heidemeyer, Hauke and Röhrig, Rainer and Hölzle, Frank and
Badeli, Vahid and Krieger, Kathrin and Gunzer, Matthias and
Chen, Jianxu and van Meegdenburg, Timo and Dada, Amin and
Balzer, Miriam and Fragemann, Jana and Jonske, Frederic and
Rempe, Moritz and Malorodov, Stanislav and Bahnsen, Fin H
and Seibold, Constantin and Jaus, Alexander and Marinov,
Zdravko and Jaeger, Paul F and Stiefelhagen, Rainer and
Santos, Ana Sofia and Lindo, Mariana and Ferreira, André
and Alves, Victor and Kamp, Michael and Abourayya, Amr and
Nensa, Felix and Hörst, Fabian and Brehmer, Alexander and
Heine, Lukas and Hanusrichter, Yannik and Weßling, Martin
and Dudda, Marcel and Podleska, Lars E and Fink, Matthias A
and Keyl, Julius and Tserpes, Konstantinos and Kim,
Moon-Sung and Elhabian, Shireen and Lamecker, Hans and
Zukić, Dženan and Paniagua, Beatriz and Wachinger,
Christian and Urschler, Martin and Duong, Luc and
Wasserthal, Jakob and Hoyer, Peter F and Basu, Oliver and
Maal, Thomas and Witjes, Max J H and Schiele, Gregor and
Chang, Ti-Chiun and Ahmadi, Seyed-Ahmad and Luo, Ping and
Menze, Bjoern and Reyes, Mauricio and Deserno, Thomas M and
Davatzikos, Christos and Puladi, Behrus and Fua, Pascal and
Yuille, Alan L and Kleesiek, Jens and Egger, Jan},
title = {{M}ed{S}hape{N}et - a large-scale dataset of 3{D} medical
shapes for computer vision.},
journal = {Biomedical engineering},
volume = {70},
number = {1},
issn = {0013-5585},
address = {Berlin [u.a.]},
publisher = {de Gruyter},
reportid = {DZNE-2025-00291},
pages = {71 - 90},
year = {2025},
abstract = {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.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.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.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/.},
keywords = {Humans / Algorithms / Imaging, Three-Dimensional: methods /
Brain Neoplasms: diagnostic imaging / Printing,
Three-Dimensional / 3D medical shapes (Other) / anatomy
education (Other) / augmented reality (Other) / benchmark
(Other) / shapeomics (Other) / virtual reality (Other)},
cin = {AG Schreiber / AG Speck},
ddc = {610},
cid = {I:(DE-2719)1310010 / I:(DE-2719)1340009},
pnm = {353 - Clinical and Health Care Research (POF4-353)},
pid = {G:(DE-HGF)POF4-353},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:39733351},
doi = {10.1515/bmt-2024-0396},
url = {https://pub.dzne.de/record/276343},
}
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