生物医学工程学杂志

生物医学工程学杂志

结合深度可分离卷积与通道加权的全卷积神经网络视网膜图像血管分割

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糖尿病和高血压等疾病会引起视网膜血管的形状发生变化,眼底图像血管分割是疾病定量分析过程中的关键步骤,对临床疾病的分析和诊断具有指导意义。本文提出一种结合深度可分离卷积与通道加权的全卷积神经网络(FCN)视网膜图像血管分割方法。首先,对眼底图像的绿色通道进行 CLAHE 及 Gamma 校正以增强对比度;然后,为了适应网络训练,对增强后的图像进行分块以扩充数据;最后,以深度可分离卷积代替标准的卷积方式以增加网络宽度,同时引入通道加权模块,以学习的方式显式地建模特征通道的依赖关系,提高特征的可分辨性。将二者结合应用于 FCN 网络中,以专家手动标识结果作为监督在 DRIVE 数据库进行实验。结果表明,本文方法在 DRIVE 库的分割准确性能够达到 0.963 0,AUC 达到 0.983 1,在 STARE 库的分割准确性可以达到 0.962 0,AUC 达到 0.983 0。在一定程度上,本文方法具有更好的特征分辨性,分割性能较好。

Diseases such as diabetes and hypertension can lead to change the shape of the retinal blood vessels. Segmentation of fundus images is a key step in the process of quantitative analysis of the disease, which is instructive in the analysis and diagnosis of clinical diseases. In this paper, a method for the segmentation of retinal image vessels based on fully convolutional network (FCN) with depthwise separable convolution and channel weighting is presented. Firstly, CLAHE and Gamma correction of the green channel of the fundus image are used to enhance the contrast. Then, in order to adapt to network training, the enhanced image is divided into patches to expand the data. Finally, the depthwise separable convolution instead of the standard convolution method is used to increase the network width. Meanwhile, the channel weighting module is introduced to explicitly model the relationship between the characteristic channels in order to improve the distinguishability of the features. The combination of them is applied to the FCN and the results of expert manual identification are used to supervise the experiment on the DRIVE database. The results show that the segmentation accuracy of the proposed method in DRIVE database reached 0.963 0 and AUC reached 0.983 1. The segmentation accuracy in STARE database reached 0.962 0 and AUC achieved 0.983 0. To some extent, the proposed method has better feature resolution and better segmentation performance.

关键词: 视网膜血管分割; 全卷积神经网络; 深度可分离卷积; 通道加权

Key words: segmentation of retinal blood vessels; fully convolutional network; depthwise separable convolution; channel weighting

引用本文: 耿磊, 邱玲, 吴骏, 肖志涛, 张芳. 结合深度可分离卷积与通道加权的全卷积神经网络视网膜图像血管分割. 生物医学工程学杂志, 2019, 36(1): 107-115. doi: 10.7507/1001-5515.201801054 复制

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