引入双编码器模型的OCT视网膜图像分割  

Study on retinal OCT segmentation with dual-encoder

作　　者：陈明惠[1] 王腾 袁媛[1] 柯舒婷 Chen Minghui;Wang Teng;Yuan Yuan;Ke Shuting(Shanghai Engineering Research Center of Interventional Medical,Shanghai Institute for Interventional Medical Devices,School of Health Science and Engineering College of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200082,China)

机构地区：[1]上海理工大学健康科学与工程学院上海介入医疗器械工程技术研究中心教育部医学光学工程中心,上海200082

出　　处：《光电工程》2023年第10期28-36,共9页Opto-Electronic Engineering

基　　金：上海市科委产学研医项目(15DZ1940400)。

摘　　要：OCT视网膜图像中存在着噪声和散斑,单一的提取空间特征往往容易遗漏一些重要信息,导致不能准确地分割目标区域。而OCT图像本身存在光谱频域特征,针对OCT图像的频域特征,本文基于U-Net和快速傅立叶卷积提出一种新的双编码器模型以提高对OCT图像视网膜层、液体的分割性能,提出的频域编码器可以提取图像频域信息并通过快速傅里叶卷积转换为空间信息,将很好地弥补单一空间编码器遗漏特征信息的不足。经过与其他经典模型的对比和消融实验,结果表明,随着频域编码器的添加,该模型能有效提升对视网膜层和液体的分割性能,平均Dice系数和mIoU相较于U-Net均提高2%,相较于ReLayNet分别提高8%和4%,其中对液体的分割提升尤为明显,相较于U-Net模型Dice系数提高了10%。There are noises and speckles in OCT retinal images,and a single extraction of spatial features is often easy to miss some important information.Therefore,the target region cannot be accurately segmented.OCT images themselves have spectral frequency domain characteristics.Aiming at the frequency domain characteristics of OCT images,this paper proposes a new dual encoder model based on U-Net and fast Fourier convolution to improve the segmentation performance of the retinal layer and liquid in OCT images.The proposed frequency encoder can extract image frequency domain information and convert it into spatial information through fast Fourier convolution.The lack of feature information that can be omitted by a single space encoder will be well-complemented.After comparison with other classical models and ablation experiments,the results show that with the addition of a frequency domain encoder,the model can effectively improve the segmentation performance of the retinal layer and liquid.Both average Dice coefficient and mIoU are increased by 2%compared with U-Net.They are increased by 8%and 4%compared with ReLayNet,respectively.Among them,the improvement of liquid segszmentation is particularly obvious,and the Dice coefficient is increased by 10%compared with the U-Net model.

关 键 词：光学相干层析成像 卷积神经网络 图像分割 双编码器 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]