The MOE Key Laboratory for Neuroinformation, Radiation Oncology Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, China.
Comput Biol Med. 2023 Jun;160:106924. doi: 10.1016/j.compbiomed.2023.106924. Epub 2023 Apr 20.
The geometric morphology of retinal vessels reflects the state of cardiovascular health, and fundus images are important reference materials for ophthalmologists. Great progress has been made in automated vessel segmentation, but few studies have focused on thin vessel breakage and false-positives in areas with lesions or low contrast. In this work, we propose a new network, differential matched filtering guided attention UNet (DMF-AU), to address these issues, incorporating a differential matched filtering layer, feature anisotropic attention, and a multiscale consistency constrained backbone to perform thin vessel segmentation. The differential matched filtering is used for the early identification of locally linear vessels, and the resulting rough vessel map guides the backbone to learn vascular details. Feature anisotropic attention reinforces the vessel features of spatial linearity at each stage of the model. Multiscale constraints reduce the loss of vessel information while pooling within large receptive fields. In tests on multiple classical datasets, the proposed model performed well compared with other algorithms on several specially designed criteria for vessel segmentation. DMF-AU is a high-performance, lightweight vessel segmentation model. The source code is at https://github.com/tyb311/DMF-AU.
视网膜血管的几何形态反映了心血管健康状况，眼底图像是眼科医生的重要参考资料。血管自动分割技术已经取得了很大的进展，但很少有研究关注病变或对比度低区域的细血管断裂和假阳性问题。在这项工作中，我们提出了一种新的网络，差分匹配滤波引导注意力 U-Net（DMF-AU），以解决这些问题，该网络结合了差分匹配滤波层、特征各向异性注意力和多尺度一致性约束骨干，以进行细血管分割。差分匹配滤波用于局部线性血管的早期识别，生成的粗糙血管图引导骨干学习血管细节。特征各向异性注意力在模型的每个阶段增强空间线性的血管特征。多尺度约束在大感受野内池化时减少血管信息的损失。在多个经典数据集上的测试中，与其他算法相比，所提出的模型在几个专门设计的血管分割标准上表现良好。DMF-AU 是一个高性能、轻量级的血管分割模型。源代码位于 https://github.com/tyb311/DMF-AU。
