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.