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Blood vessels in color retinal fundus images, captured using fundus cameras, are often affected by illumination variations and noise, making it difficult to preserve vascular integrity and posing a significant challenge for vessel segmentation. In this paper, we propose HM-Mamba, a novel hierarchical multi-scale Mamba-based architecture that incorporates tubular structure-aware convolution to extract both local and global vascular features for retinal vessel segmentation. First, we introduce a tubular structure-aware convolution to reinforce vessel continuity and integrity. Building on this, we design a multi-scale fusion module that aggregates features across varying receptive fields, enhancing the model\u2019s robustness in representing both primary trunks and fine branches. Second, we integrate multi-branch Fourier transform with the dynamic state modeling capability of Mamba to capture both long-range dependencies and multi-frequency information. This design enables robust feature representation and adaptive fusion, thereby enhancing the network\u2019s ability to model complex spatial patterns. Furthermore, we propose a hierarchical multi-scale interactive Mamba block that integrates multi-level encoder features through gated Mamba-based global context modeling and residual connections, enabling effective multi-scale semantic fusion and reducing detail loss during downsampling. Extensive evaluations on five widely used benchmark datasets\u2014DRIVE, CHASE_DB1, STARE, IOSTAR, and LES-AV\u2014demonstrate the superior performance of HM-Mamba, yielding Dice coefficients of 0.8327, 0.8197, 0.8239, 0.8307, and 0.8426, respectively.<\/jats:p>","DOI":"10.3390\/e27080862","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T15:44:21Z","timestamp":1755186261000},"page":"862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Multi-Scale Mamba with Tubular Structure-Aware Convolution for Retinal Vessel Segmentation"],"prefix":"10.3390","volume":"27","author":[{"given":"Tao","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}]},{"given":"Dongyuan","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0825-7188","authenticated-orcid":false,"given":"Haonan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"}]},{"given":"Jiamin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Science, Jimei University, Xiamen 361021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1168-3527","authenticated-orcid":false,"given":"Weijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"}]},{"given":"Chunpei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Guangxi Normal University, Guilin 541001, China"}]},{"given":"Guixia","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2019). 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