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Imaging"],"abstract":"<jats:p>Retinal vessel segmentation is critical for early diagnosis of diabetic retinopathy, yet existing deep models often compromise accuracy for complexity. We propose DSAE-Net, a lightweight dual-stage network that addresses this challenge by (1) introducing a Parameterized Cascaded W-shaped Architecture enabling progressive feature refinement with only 1% of the parameters of a standard U-Net; (2) designing a novel Skeleton Distance Loss (SDL) that overcomes boundary loss limitations by leveraging vessel skeletons to handle severe class imbalance; (3) developing a Cross-modal Fusion Attention (CMFA) module combining group convolutions and dynamic weighting to effectively expand receptive fields; and (4) proposing Coordinate Attention Gates (CAGs) to optimize skip connections via directional feature reweighting. Evaluated extensively on DRIVE, CHASE_DB1, HRF, and STARE datasets, DSAE-Net significantly reduces computational complexity while outperforming state-of-the-art lightweight models in segmentation accuracy. Its efficiency and robustness make DSAE-Net particularly suitable for real-time diagnostics in resource-constrained clinical settings.<\/jats:p>","DOI":"10.3390\/jimaging11090306","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T10:29:07Z","timestamp":1757327347000},"page":"306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Retinal Vessel Segmentation with 78K Parameters"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhigao","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6235-6555","authenticated-orcid":false,"given":"Jiakai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]},{"given":"Xianming","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]},{"given":"Kaixi","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9509-0755","authenticated-orcid":false,"given":"Xinpan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]},{"given":"Yanhui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.bspc.2012.05.005","article-title":"Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation","volume":"8","author":"Fathi","year":"2013","journal-title":"Biomed. 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