Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation

Jingyu Zhao, Jiong Zhang, Bin Deng, Yalin Zheng, Jiang Liu, Ran Song, Yitian Zhao

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Morphological changes, e.g. thickness of retinal or choroidal layers in Optical coherence tomography (OCT), is of great importance in clinic applications as they reveal some specific eye diseases and other systemic conditions. However, there are many challenges in the accurate segmentation of retinal and choroidal layers, such as low contrast between different tissue layers and variations between images acquired from multiple devices. There is a strong demand on accurate and robust segmentation models with high generalization ability to deal with images from different devices. This paper proposes a new unsupervised guided adversarial adaptation (GAA) network to segment both retinal layers and the choroid in OCT images. To our best knowledge, this is the first work to extract retinal and choroidal layers in a unified manner. It first introduces a dual encoder structure to ensure that the encoding path of the source domain image is independent of that of the target domain image. By integrating the dual encoder into an adversarial framework, the holistic GAA network significantly alleviates the performance degradation of the source domain image segmentation caused by parameter entanglement with the encoder of the target domain and also improves the segmentation performance of the target domain images. Experimental results show that the proposed network outperforms other state-of-the-art methods in retinal and choroidal layer segmentation.

Original languageEnglish
Title of host publicationOphthalmic Medical Image Analysis - 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages82-91
Number of pages10
ISBN (Print)9783030869991
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12970 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Domain adaptation
  • OCT
  • Retinal and choroidal layer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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