带有注意力机制的OCTA视网膜血管分割方法  

Segmentation Method of Retinal Vessels in OCTA with Attention Mechanism

作　　者：崔少国 文浩 张宇楠 唐艺菠 杜兴[1] CUI Shaoguo;WEN Hao;ZHANG Yunan;TANG Yibo;DU Xing(School of Computer and Information Sciences,Chongqing Normal University,Chongqing 401331,China)

机构地区：[1]重庆师范大学计算机与信息科学学院,重庆401331

出　　处：《计算机工程与应用》2023年第18期163-171,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(62003065);重庆师范大学基金项目(20XLB004);重庆市科技局项目(cstc2019jscx-mbdxX0061);教育部人文社科项目(18XJC880002);重庆市教委科技项目(KJQN201800539,KJQN202000510)。

摘　　要：视网膜血管分割是智能辅助诊断过程中的关键步骤。由于血管末端细小且易与背景混淆,导致很难精确分割。针对此类问题,提出一种基于深度可分离卷积与块注意力机制的高效视网膜血管分割算法。使用步长为2的卷积层代替最大池化层进行特征筛选;为了降低网络的参数量,用深度可分离卷积代替常规的卷积;引入注意力机制学习重要特征,对OCTA视网膜血管图像的像素进行精确分类。将该方法在2020版的公开数据集OCTA-500上进行充分实验。结果表明,该方法在分割性能指标F1、mIoU、Se、Sp、Acc和Pre上分别达到了80.01%、81.18%、84.39%、96.41%、95.32%和76.24%;和基准方法U-net相比,该方法的参数量和FLOPs也显著降低,分别只有U-net的19.2%和16.5%。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 prob-lem,an efficient retinal vessel segmentation algorithm based on deep separable convolution and block attention mecha-nism 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.

关 键 词：深度学习 医学图像处理 视网膜血管分割 深度可分离卷积 块注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]