HMS-VesselNet：具有拓扑保持损失的层次多尺度注意网络用于视网膜血管分割
HMS-VesselNet: Hierarchical Multi-Scale Attention Network with Topology-Preserving Loss for Retinal Vessel Segmentation
AI总结：
本文提出HMS-VesselNet，通过多尺度网络和拓扑保持损失提升视网膜血管分割的精度，尤其改善了薄周缘血管的检测性能。
摘要：
基于标准重叠损失的视网膜血管分割方法常因薄周缘血管占据像素少且对比度低而遗漏。本文提出HMS-VesselNet，通过四个并行分支在不同分辨率下处理眼底图像，并结合学习融合权重。训练损失结合Dice、二元交叉熵和中心线Dice，联合优化区域重叠和血管连续性。从第20个epoch开始应用困难示例挖掘，集中梯度更新在最困难的训练图像上。在DRIVE、STARE和CHASE_DB1的68张图像上使用5折交叉验证测试，模型达到平均Dice系数88.72±0.67%，灵敏度90.78±1.42%，AUC 98.25±0.21%。在留一数据集验证中，AUC在每个未见数据集上均高于95%。最大改进体现在薄周缘血管的召回率上，这些结构是标准方法最常遗漏且对糖尿病视网膜病变早期检测最关键的部位。
英文摘要：
Retinal vessel segmentation methods based on standard overlap losses tend to miss thin peripheral vessels because these structures occupy very few pixels and have low contrast against the background. We propose HMS-VesselNet, a hierarchical multi-scale network that processes fundus images across four parallel branches at different resolutions and combines their outputs using learned fusion weights. The training loss combines Dice, binary cross-entropy, and centerline Dice to jointly optimize area overlap and vessel continuity. Hard example mining is applied from epoch 20 onward to concentrate gradient updates on the most difficult training images. Tested on 68 images from DRIVE, STARE, and CHASE_DB1 using 5-fold cross-validation, the model achieves a mean Dice of 88.72 +/- 0.67%, Sensitivity of 90.78 +/- 1.42%, and AUC of 98.25 +/- 0.21%. In leave-one-dataset-out experiments, AUC remains above 95% on each unseen dataset. The largest improvement is in the recall of thin peripheral vessels, which are the structures most frequently missed by standard methods and most critical for early detection of diabetic retinopathy.
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。
