Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search

Abstract

Accurate quantitative analysis of the retinal layer in optical coherence tomography (OCT) images plays a crucial role in detecting and diagnosing ocular diseases. In this paper, we present a novel automatic method by combining multiscale convolutional neural network (MCNN) and graph search to accurately segment multiple retinal layer boundaries in OCT images. Firstly, we propose a MCNN architecture to extract multiscale features of retinal layer boundaries and thus to produce probability maps of the retinal layer boundaries. Especially, we construct a MCNN architecture by fusing feature maps extracted from different sizes of input image patches to learn multiscale information about the retinal layer boundaries. Meanwhile, we distinguish the background pixels based on the location information to reduce the probability that the network misclassifies the background as a target. Furthermore, we propose an improved graph search algorithm to detect the final layer boundaries from the probability maps. Finally, we evaluate our proposed method with eight state-of-the-art approaches on a publicly OCT dataset with age-related macular degeneration (AMD). The experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in terms of quantitative results and visual effects.