基于全尺度跳跃连接的视网膜血管分割算法

为了解决经典分割算法对于视网膜血管分割精度不够的缺陷,通过将U-net3+(全尺度连接U形网络)应用于视网膜微血管分割,并加以改进来提高分割精度。首先利用U-net3+中的全尺度跳跃连接,提取更多尺度的视网膜微血管特征。针对细小血管难以捕捉的问题,将网络中的普通卷积换成可变卷积,它可以根据血管的形状、大小改变感受野的大小,提高算法的分割准确度。然后使用SFAM模块来优化U-net3+网络中的特征融合部分,保留更多的有用信息。在视网膜图像数据库上测试本文算法,结果表明,分割的平均准确率为97.63%,比传统的U-net网络和U-net3+网络分别提高了2.35%、0.99%。可见,改进算法有效提高了视网膜血管分割精度。

RETINAL VESSELS SEGMENTATION ALGORITHM BASED ON IMPROVED U-NET3+ NETWORK

In order to solve the defect that the classical segmentation algorithm is not accurate enough for retinal vascular segmentation, U-net3+ (full-scale connected U-shaped network) is applied to retinal microvascular segmentation and improved to improve the segmentation accuracy. Firstly, the full-scale jump connection in U-net3 + is used to extract more scale retinal microvascular features. Aiming at the problem that it is difficult to capture small blood vessels, the ordinary convolution in the network is replaced by deformable convolution, which can change the size of receptive field according to the shape and size of blood vessels and improve the segmentation accuracy of the algorithm. Then SFAM module is used to optimize the feature fusion part in U-net3+ network to retain more useful information. The algorithm is tested on the retinal image (drive) database. The results show that the average accuracy (ACC) of segmentation is 97.63%, which is 2.35% and 0.99% higher than the traditional u-net network and u-net3 + network respectively. It can be seen that the improved algorithm effectively improves the segmentation accuracy.