论文标题
基于视网膜血管分割的U形网络的MC-UNET多模块串联
MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation
论文作者
论文摘要
对视网膜血管的准确分割是眼科疾病临床诊断的重要一步。许多深度学习框架已经用于视网膜血管分割任务。但是，复杂的血管结构和不确定的病理特征使血管分割仍然非常具有挑战性。本文提出了一个基于非常卷积和多内核合并的新型U形网络，该网络称为多模型串联，在本文中提出了视网膜血管分割。提出的网络结构保留了三层U-NET的基本结构，其中将多内核池块的非常卷积设计为获得更多的上下文信息。空间注意模块与致密的卷积模块和多内核合并模块串联以形成多模块的串联。通过级联以获取非常卷积的较大接受场来选择不同的扩张率。在这些公共视网膜数据集上进行了足够的比较实验：驱动器，凝视和chase_db1。结果表明，该方法有效，尤其是对于微丝。该代码将在https://github.com/rebeccala/mc-unet上发布
Accurate segmentation of the blood vessels of the retina is an important step in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make the blood vessel segmentation still very challenging. A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper. The proposed network structure retains three layers the essential structure of U-Net, in which the atrous convolution combining the multi-kernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with dense atrous convolution module and multi-kernel pooling module to form a multi-module concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be put out at https://github.com/Rebeccala/MC-UNet
