Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model

作     者：Wang, Hongqiu Chen, Yixian Chen, Wu Xu, Huihui Zhao, Haoyu Sheng, Bin Fu, Huazhu Yang, Guang Zhu, Lei 

作者机构： Guangzhou511400 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu610072 China School of Computer Science Wuhan University Wuhan430000 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China  138632 Singapore Bioengineering/Imperial-X Imperial College London United Kingdom Systems Hub Hong Kong University of Science and Technology Guangzhou China The Hong Kong University of Science and Technology Hong Kong 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Pixels 

摘      要：Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically 200 spanning degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies have revealed that the selective State Space Model (SSM) in Mamba performs well in modeling long-range dependencies, which is crucial for capturing the continuity of elongated vessel structures. 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 snakelike 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 shall be released upon publication of this work. Copyright © 2024, The Authors. All rights reserved.