arXiv 2504.135532025-04-18eess.IVcs.CV

基于动态长程依赖与多尺度视网膜边缘融合增强的视网膜血管分割新混合方法

A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

School of Artificial Intelligence（人工智能学院）；Hebei University of Technology（河北工业大学）；NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases（国家卫生健康委员会激素与发育重点实验室和天津代谢疾病重点实验室）；Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology（天津医科大学楚生一纪念医院及内分泌研究所）

Yihao Ouyang, Xunheng Kuang, Mengjia Xiong, Zhida Wang, Yuanquan Wang

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对现有视网膜血管分割方法在多尺度变异性、复杂曲率等方面的局限，提出结合CNNs与Mamba的混合框架，通过三项创新提升精度，在公开数据集上实现SOTA性能，为临床应用提供鲁棒方法。

摘要：

准确的视网膜血管分割为眼科图像分析提供了关键的结构信息。然而，现有方法在应对多尺度血管变异性、复杂曲率和模糊边界等挑战时存在困难。尽管卷积神经网络（CNNs）、基于Transformer的模型和基于Mamba的架构推动了该领域的发展，但它们常面临血管不连续或边缘特征模糊的问题。为解决这些局限，我们提出一种新的混合框架，协同集成CNNs和Mamba以实现高精度视网膜血管分割。我们的方法引入三个关键创新：1）所提出的高分辨率边缘融合网络是一种保留高分辨率的混合分割框架，结合多尺度骨干网络与多尺度视网膜边缘融合（MREF）模块以增强边缘特征，确保准确且鲁棒的血管分割；2）动态蛇形视觉状态空间块结合动态蛇形卷积与Mamba，自适应捕捉血管曲率细节和长程依赖，改进的八向二维蛇形选择扫描机制和动态加权策略增强了对复杂血管拓扑的感知；3）MREF模块通过多尺度边缘特征聚合提升边界精度，在抑制噪声的同时强调跨尺度的关键血管结构。在三个公开数据集上的实验表明，我们的方法实现了最先进的性能，尤其在维持血管连续性和有效分割低对比度区域血管方面表现突出。这项工作为需要准确视网膜血管分析的临床应用提供了一种鲁棒方法。代码可在https://github.com/frank-oy/HREFNet获取。

英文摘要：

Accurate retinal vessel segmentation provides essential structural information for ophthalmic image analysis. However, existing methods struggle with challenges such as multi-scale vessel variability, complex curvatures, and ambiguous boundaries. While Convolutional Neural Networks (CNNs), Transformer-based models and Mamba-based architectures have advanced the field, they often suffer from vascular discontinuities or edge feature ambiguity. To address these limitations, we propose a novel hybrid framework that synergistically integrates CNNs and Mamba for high-precision retinal vessel segmentation. Our approach introduces three key innovations: 1) The proposed High-Resolution Edge Fuse Network is a high-resolution preserving hybrid segmentation framework that combines a multi-scale backbone with the Multi-scale Retina Edge Fusion (MREF) module to enhance edge features, ensuring accurate and robust vessel segmentation. 2) The Dynamic Snake Visual State Space block combines Dynamic Snake Convolution with Mamba to adaptively capture vessel curvature details and long-range dependencies. An improved eight-directional 2D Snake-Selective Scan mechanism and a dynamic weighting strategy enhance the perception of complex vascular topologies. 3) The MREF module enhances boundary precision through multi-scale edge feature aggregation, suppressing noise while emphasizing critical vessel structures across scales. Experiments on three public datasets demonstrate that our method achieves state-of-the-art performance, particularly in maintaining vascular continuity and effectively segmenting vessels in low-contrast regions. This work provides a robust method for clinical applications requiring accurate retinal vessel analysis. The code is available at https://github.com/frank-oy/HREFNet.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。