Disentangled Representations for Domain-generalized Cardiac Segmentation

Xiao Liu, Spyridon Thermos, Agis Chartsias, Alison O'Neil, Sotirios Tsaftaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of dfferent domains. Since the acquisition and annotation of medical data are costly and time-consuming, recent work focuses on domain adaptation and generalization to bridge the gap between data from different populations and scanners. In this paper, we propose two data augmentation methods that focus on improving the domain adaptation and generalization abilities of state-to-the-art cardiac segmentation models. In particular, our \Resolution Augmentation"
method generates more diverse data by rescaling images to different resolutions within a range spanning different scanner protocols. Subsequently, our \Factor-based Augmentation" method generates more diverse data by projecting the original samples onto disentangled latent spaces, and combining the learned anatomy and modality factors from different domains. Our extensive experiments demonstrate the importance of effcient adaptation between seen and unseen domains, as well as model generalization ability, to robust cardiac image segmentation.
Original languageEnglish
Title of host publicationSTACOM: International Workshop on Statistical Atlases and Computational Models of the Heart
Subtitle of host publicationStatistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges
PublisherSpringer
Pages187-195
ISBN (Electronic)978-3-030-68107-4
ISBN (Print)978-3-030-68106-7
DOIs
Publication statusE-pub ahead of print - 29 Jan 2021
Event23rd International Conference on Medical Image Computing and Computer Assisted Intervention - Lima, Peru
Duration: 4 Oct 20208 Oct 2020
Conference number: 23
https://www.miccai2020.org/en/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12592
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20
Internet address

Keywords

  • Cardiac image segmentation
  • Data augmentation
  • Disentangled factors mixing
  • Domain adaptation
  • Domain generalization

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