Logo
Nazad
Jianning Li, Antonio Pepe, C. Gsaxner, Gijs Luijten, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, G. Melito, A. R. Memon, Xiaojun Chen, Jan S. Kirschke, E. D. L. Rosa, Patrich Ferndinand Christ, Hongwei Li, David G. Ellis, M. Aizenberg, S. Gatidis, T. Kuestner, N. Shusharina, N. Heller, V. Andrearczyk, A. Depeursinge, M. Hatt, A. Sekuboyina, Maximilian Loeffler, H. Liebl, R. Dorent, Tom Kamiel Magda Vercauteren, J. Shapey, A. 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, E. Garza-Villarreal, T. Balducci, Diego Angeles-Valdez, R. Souza, L. Rittner, R. Frayne, Yuanfeng Ji, S. Chatterjee, A. Nuernberger, J. Pedrosa, Carlos A. Ferreira, Guilherme Aresta, António Cunha, A. Campilho, Yannick Suter, Jose A Garcia, A. Lalande, E. Audenaert, C. Krebs, T. V. Leeuwen, E. Vereecke, R. Roehrig, F. Hoelzle, V. Badeli, Kathrin Krieger, M. Gunzer, Jianxu Chen, Amin Dada, M. Balzer, Jana Fragemann, F. Jonske, Moritz Rempe, Stanislav Malorodov, F. Bahnsen, C. Seibold, A. Jaus, A. Santos, M. Lindo, André Ferreira, V. Alves, Michael Kamp, Amr Abourayya, F. Nensa, Fabian Hoerst, Alexandra Brehmer, Lukas Heine, L. Podleska, M. Fink, J. Keyl, K. Tserpes, Moon S Kim, Shireen Elhabian, H. Lamecker, Dženan Zukić, B. Paniagua, C. Wachinger, M. Urschler, Luc Duong, Jakob Wasserthal, P. Hoyer, Oliver Basu, T. Maal, M. Witjes, Ping Luo, Bjoern H Menze, M. Reyes, C. Davatzikos, B. Puladi, J. Kleesiek, J. Egger
13 30. 8. 2023.

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

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


Pretplatite se na novosti o BH Akademskom Imeniku

Ova stranica koristi kolačiće da bi vam pružila najbolje iskustvo

Saznaj više