凝视细节：用于OCTA视网膜血管分割的局部敏感增强
Gaze into the Details: Locality-Sensitive Enhancement for OCTA Retinal Vessel Segmentation
AI总结：
本文提出LSENet，通过引入三个创新模块解决OCTA血管分割中局部对比度低导致的断续和细节丢失问题，实验表明其在多个公开数据集上达到最佳性能且参数更少。
摘要：
现有的OCTA血管分割深度学习框架大多基于U-Net架构，但大多数方法仅关注整体表示，难以处理OCTA特有的低局部对比度问题，导致血管断续和细节丢失。为此，我们提出LSENet，基于U-Net架构引入三个核心创新模块：为解决血管断续问题，引入补丁信息增强模块（PIE），用补丁级注意力替代标准跳接连接；为缓解细节丢失问题，提出多尺度特征融合模块（MFF），通过从原始输入和前一层提取可解释特征，为PIE模块提供丰富多尺度信息；最后设计连接性细化解码器（CRD），通过最终卷积层的大核减少碎片化。在三个公开数据集（OCTA-500、ROSE-1和ROSSA）上的实验表明，所提LSENet在性能上达到最佳，且参数更少。
英文摘要：
Existing deep learning frameworks for Optical Coherence Tomography Angiography (OCTA) vessel segmentation are largely derived from the U-Net architecture, which serves as the foundation for most current designs. However, most of these methods focus only on holistic representation, struggling to address the problem of low local contrast unique to OCTA, which leads to vessel discontinuities and loss of detail. To address these problems, we propose LSENet, which builds upon the U-Net architecture by introducing three core innovative modules: To address vessel discontinuities, we introduce the Patch Information Enhance module (PIE), which replaces standard skip connections to execute patch-wise attention. To mitigate detail loss, the Multiscale Feature Fusion module (MFF) is proposed to feed the PIE module rich, multi-scale information by extracting visually interpretable features from both the original input and preceding layers. Finally, the Connectivity Refinement Decoder (CRD) is designed to refine features from all levels and utilize a large kernel in the final convolutional layer to reduce fragmentation. Experiments on three public datasets (OCTA-500, ROSE-1, and ROSSA) demonstrate that our proposed LSENet achieves state-of-the-art performance while requiring fewer parameters.
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。
