Style-based manifold for weakly-supervised disease characteristic discovery

Siyu Liu, Linfeng Liu, Craig Engstrom, Xuan Vinh To, Zongyuan Ge, Stuart Crozier, Fatima Nasrallah, Shekhar S. Chandra

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

In Alzheimer’s Disease (AD), interpreting tissue changes is key to discovering disease characteristics. However, AD-induced brain atrophy can be difficult to observe without Cognitively Normal (CN) reference images, and collecting co-registered AD and CN images at scale is not practical. We propose Disease Discovery GAN (DiDiGAN), a style-based network that can create representative reference images for disease characteristic discovery. DiDiGAN learns a manifold of disease-specific style codes. In the generator, these style codes are used to “stylize” an anatomical constraint into synthetic reference images (for various disease states). The constraint in this case underpins the high-level anatomical structure upon which disease features are synthesized. Additionally, DiDiGAN’s manifold is smooth such that seamless disease state transitions are possible via style interpolation. Finally, to ensure the generated reference images are anatomically correlated across disease states, we incorporate anti-aliasing inspired by StyleGAN3 to enforce anatomical correspondence. We test DiDiGAN on the ADNI dataset involving CN and AD magnetic resonance images (MRIs), and the generated reference AD and CN images reveal key AD characteristics (hippocampus shrinkage, ventricular enlargement, cortex atrophies). Moreover, by interpolating DiDiGAN’s manifold, smooth CN-AD transitions were acquired further enhancing disease visualization. In contrast, other methods in the literature lack such dedicated disease manifolds and fail to synthesize usable reference images for disease characteristic discovery.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part V
EditorsHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Place of PublicationCham Switzerland
PublisherSpringer
Pages368-378
Number of pages11
ISBN (Electronic)9783031439049
ISBN (Print)9783031439032
DOIs
Publication statusPublished - 2023
EventMedical Image Computing and Computer-Assisted Intervention 2023 - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023
Conference number: 26th
https://link.springer.com/book/10.1007/978-3-031-43901-8 (Proceedings)
https://conferences.miccai.org/2023/en/ (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14224
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMedical Image Computing and Computer-Assisted Intervention 2023
Abbreviated titleMICCAI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23
Internet address

Keywords

  • Alzheimer’s Disease
  • Disease learning
  • Weak supervision

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