Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data

Haojin Li, Heng Li, Hai Shu, Jianyu Chen, Yan Hu, Jiang Liu

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


The morphology of the retinal vascular structure in fundus images is of great importance for ocular disease diagnosis. However, due to the poor fundus image quality and domain shifts between datasets, retinal vessel segmentation has long been regarded as a problematic machine-learning task. This work proposes a novel algorithm High-frequency Guided Cascaded Network (HGC-Net) to address the above issues. In our algorithm, a self-supervision mechanism is designed to improve the generalizability and robustness of the model. We apply Fourier Augmented Co-Teacher (FACT) augmentation to convert the style of fundus images, and extract high-frequency component (HFC) to highlight the vascular structure. The main structure of the algorithm is two cascaded U-nets, in which the first U-net generates a domain-invariant high-frequency map of fundus images, thus improving the segmentation stability of the second U-net. Comparison with the state-of-the-art methods and ablation study are conducted to demonstrate the excellent performance of our proposed HGC-Net.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023


  • Retinal vessel segmentation
  • domain generalization
  • self-supervision

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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