提出一种结合卷积神经网络(CNN)和改进的图搜索来分割光学相干断层扫描成像(OCT)图像中的7个视网膜层边界的方法。首先利用CNN自动提取每个边界的特征并训练相应的分类器,由此将获得的每个边界的概率图作为分割的感兴趣区域;其次,提出一种改进的图搜索方法,该方法在垂直梯度的基础上添加了横向约束,当遇到血管阴影时,分割线可以横向穿过阴影。使用所提方法对正常图像进行分割,并对得到的结果和图搜索方法、基于CNN的方法得到的结果进行比较。实验结果表明,所提方法能精确分割7个视网膜层边界,平均层边界误差为(4.31±5.87) μm。

Herein, a method that combines convolutional neural networks (CNNs) and improved graph search is proposed to segment seven retinal-layer boundaries in optical coherence tomography (OCT) images. First, CNN is used to extract the features of each boundary automatically and to train the corresponding classifier to obtain the probability map of each boundary as the region of interest for boundary segmentation. Second, an improved graph search method is proposed to add lateral constraints based on the vertical gradient. When encountering a vascular shadow, the segmentation line can laterally cross the shadow. The normal image is segmented using the proposed method, and the results are compared with those obtained using the graph search method and the method based on CNN. Experimental results show that the proposed method can accurately segment seven retinal-layer boundaries with an average layer boundary error of (4.31±5.87)μm.