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

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

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

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.

源语言英语
主期刊名2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
出版商IEEE Computer Society
ISBN(电子版)9781665473583
DOI
出版状态已出版 - 2023
已对外发布
活动20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, 哥伦比亚
期限: 18 4月 202321 4月 2023

出版系列

姓名Proceedings - International Symposium on Biomedical Imaging
2023-April
ISSN(印刷版)1945-7928
ISSN(电子版)1945-8452

会议

会议20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
国家/地区哥伦比亚
Cartagena
时期18/04/2321/04/23

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引用此

Li, H., Li, H., Shu, H., Chen, J., Hu, Y., & Liu, J. (2023). Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data. 在 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Proceedings - International Symposium on Biomedical Imaging; 卷 2023-April). IEEE Computer Society. https://doi.org/10.1109/ISBI53787.2023.10230561