The task of 3D shape classification is closely related to finding a good representation of the shapes. In this study, we focus on surface representations of complex anatomies and on how such representations can be utilized for super- and unsupervised classification. We present a novel Implicit Neural Distance Representation based on unsigned distance fields (UDFs). The UDFs can be embedded into a low-dimensional latent space, which is optimized using only the shape itself. We demonstrate that this self-optimized latent space holds important global shape information useful for reconstructing the anatomies, but also that unsupervised clustering of the latent vectors successfully separates different anatomies (left atrium, left/right ear-canals and human faces). Finally, we show how the representation can be used to do gender classification of human face geometries, which is a notoriously hard problem.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention|
|Editors||Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert|
|Publication status||Published - 2021|
|Event||24th International Conference on Medical Image Computing and Computer Assisted Intervention - Virtual, Online|
Duration: 27 Sep 2021 → 1 Oct 2021
|Conference||24th International Conference on Medical Image Computing and Computer Assisted Intervention|
|Period||27/09/2021 → 01/10/2021|
|Series||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
Bibliographical noteFunding Information:
This work was supported by a PhD grant from the Technical University of Denmark-Department of Applied Mathematics and Computer Science (DTU Compute) and the Spanish Ministry of Science, Innovation and Universities under the Retos I+D Programme (RTI2018-101193-B-I00).
© 2021, Springer Nature Switzerland AG.
- Implicit functions
- Shape analysis
- Unsigned distance fields