发表机构
Michigan Technological University; Fairfield University; University of Dayton; Minnesota State University（密歇根理工大学; 费尔菲尔德大学; 代顿大学; 明尼苏达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出U-PEN Mamba，一种结合渐进式扩展与选择性状态空间建模的U形网络，用于高效视网膜血管分割，在CHASE DB1和DRIVE上取得最优平均交并比，验证了其全局上下文建模的有效性。
AI 中文摘要
准确的视网膜血管分割对于计算机辅助眼科分析至关重要，然而薄血管、低对比度以及严重的前景-背景不平衡仍然对编码器-解码器网络构成挑战。本文提出了U-PEN Mamba，一种U形视网膜血管分割架构，将渐进式非线性特征扩展与选择性状态空间建模相结合。所提出的网络通过渐进式扩展增强局部血管响应，通过具有线性序列复杂度的Mamba全局上下文（MGC）块建模长距离空间依赖，并使用基于注意力的解码器融合来恢复精细的血管边界。我们在CHASE DB1和DRIVE上使用一致的基于补丁的预处理流程评估U-PEN Mamba，并将其与基于卷积、注意力、Transformer和Mamba的分割基线进行比较。U-PEN Mamba在比较方法中获得了最佳的平均交并比，在CHASE DB1上达到0.8394，在DRIVE上达到0.8221，Dice分数分别为0.8187和0.8078，使用21.6M可训练参数。消融研究表明，MGC块在U-Net基线上贡献了最大的增益，而投影维度和状态大小提供了实用的精度-效率控制。这些结果表明，选择性状态空间建模是一种有前景的全局上下文机制，用于参数高效的视网膜血管分割。代码可在以下网址获取：此https URL。
英文摘要
Accurate retinal vessel segmentation is important for computer-aided ophthalmic analysis, yet thin vessels, low contrast, and severe foreground-background imbalance remain challenging for encoder-decoder networks. This paper presents U-PEN Mamba, a U-shaped retinal vessel segmentation architecture that couples progressive nonlinear feature expansion with selective state-space modeling. The proposed network enriches local vessel responses with progressive expansion, models long-range spatial dependencies through a Mamba Global Context (MGC) block with linear sequence complexity, and uses attention-based decoder fusion to recover fine vascular boundaries. We evaluate U-PEN Mamba on CHASE DB1 and DRIVE using a consistent patch-based preprocessing pipeline and compare it with convolutional, attention-based, transformer-based, and Mamba-based segmentation baselines. U-PEN Mamba obtains the best mean intersection over union among the compared methods, achieving 0.8394 on CHASE DB1 and 0.8221 on DRIVE, with Dice scores of 0.8187 and 0.8078, respectively, using 21.6M trainable parameters. Ablation studies show that the MGC block contributes the largest gain over the U-Net baseline, while projection dimension and state size provide practical accuracy-efficiency control. These results indicate that selective state-space modeling is a promising global-context mechanism for parameter-efficient retinal vessel segmentation. Code is available at: https://github.com/areyesan/UPEN_Mamba.
