Domain Adaptive Retinal Vessel Segmentation Guided by High-frequency Component

Haojin Li, Heng Li*, Zhongxi Qiu, Yan Hu, Jiang Liu

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The morphological structure of retinal fundus blood vessels is of great significance for medical diagnosis, thus the automatic retinal vessel segmentation algorithm has become one of the research hotspots in the field of medical image processing. However, there are still several unsolved difficulties in this task: the existed methods are too sensitive to the low-frequency noise in the fundus images, and there are few annotated data sets available, and meanwhile, the retinal images of different datasets vary greatly. To solve the above problems, we propose a domain adaptive vessel segmentation algorithm with multiple image entrances called MIUnet, which is robust to the etiological noises and domain shift between diverse datasets. We apply Fourier domain adaptation and the high-frequency component filtering modules to transform the raw images into two styles, and simultaneously reduce the discrepancy between the source domain and target domain retinal images. After that, images produced by the two modules are fed into a multi-input deep segmentation model, and the full utilization of features from different modalities is ensured by the deep supervision mechanism. Experiments prove that, compared with other segmentation methods, the MIUnet has better performances in cross-domain experiments, where the IoU reaches 63% when trained on ARIA dataset and tested on the DRIVE dataset and 53% in the opposite direction.

Original languageEnglish
Title of host publicationOphthalmic Medical Image Analysis - 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsBhavna Antony, Huazhu Fu, Cecilia S. Lee, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages115-124
Number of pages10
ISBN (Print)9783031165245
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event9th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sept 202222 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13576 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/2222/09/22

Keywords

  • Domain adaptation
  • Retinal fundus image
  • Retinal vessel segmentation

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