Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies

Kristine Aavild Juhl*, Xabier Morales, Ole de Backer, Oscar Camara, Rasmus Reinhold Paulsen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer
Publication date2021
Pages405-415
ISBN (Print)9783030871956
DOIs
Publication statusPublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention - Virtual, Online
Duration: 27 Sep 20211 Oct 2021

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention
CityVirtual, Online
Period27/09/202101/10/2021
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12902 LNCS
ISSN0302-9743

Bibliographical note

Funding 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).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Implicit functions
  • Shape analysis
  • Unsigned distance fields

Fingerprint

Dive into the research topics of 'Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies'. Together they form a unique fingerprint.

Cite this