Segmentation Method of Retinal Vessels in OCTA with Attention Mechanism

Retinal vascular segmentation is a key step in the process of intelligent aided diagnosis. Because the ends of the vessels are small and easy to be confused with the background, it is difficult to segment accurately. To solve this problem, an efficient retinal vessel segmentation algorithm based on deep separable convolution and block attention mechanism is proposed in this paper. Firstly, the convolution layer with step size of 2 is used to replace the maximum pooling layer for feature screening. Then, in order to reduce the complexity of the network, the general convolution operation is replaced by the depth separable convolution operation. Finally, the attention mechanism is introduced to learn important features to accurately classify the pixels of the OCTA image. The proposed method is tested on the 2020 public dataset OCTA-500. The results show that the F1, mIoU, Se, Sp, Acc and Pre are 80.01%, 81.18%, 84.39%, 96.41%, 95.32% and 76.24% respectively. Compared with the U-net, the parameters and FLOPs of this method are also significantly reduced, only 19.2% and 16.5% of the U-net. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.