In this section
@article{Wang:2025:10.1109/TMI.2025.3584468,
author = {Wang, H and Chen, Y and Chen, W and Xu, H and Zhao, H and Sheng, B and Fu, H and Yang, G and Zhu, L},
doi = {10.1109/TMI.2025.3584468},
journal = {IEEE Trans Med Imaging},
pages = {4811--4825},
title = {Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation With Selective State-Space Model.},
url = {http://dx.doi.org/10.1109/TMI.2025.3584468},
volume = {44},
year = {2025}
}
TY - JOUR
AB - Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically spanning 200 degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies highlight that Mamba's selective State Space Model (SSM) excels in modeling long-range dependencies with linear computational complexity, making it highly suitable for preserving the continuity of elongated vessel structures, especially for high-resolution UWF images. Inspired by this, we propose the Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs. Our code will be released upon publication (https://github.com/whq-xxh/Serp-Mamba).
AU - Wang,H
AU - Chen,Y
AU - Chen,W
AU - Xu,H
AU - Zhao,H
AU - Sheng,B
AU - Fu,H
AU - Yang,G
AU - Zhu,L
DO - 10.1109/TMI.2025.3584468
EP - 4825
PY - 2025///
SP - 4811
TI - Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation With Selective State-Space Model.
T2 - IEEE Trans Med Imaging
UR - http://dx.doi.org/10.1109/TMI.2025.3584468
UR - https://www.ncbi.nlm.nih.gov/pubmed/40587341
VL - 44
ER -
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