Fully convolutional network and graph-based method for co-segmentation of retinal layer on macular OCT images

Retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis and study of retinal diseases. Graph-based methods are commonly used in layer segmentation. However, most of these methods require a lot of human efforts for determining an appropriate model to compute good edge weights. In this paper, we propose a novel automatic method for segmenting retinal layers in macular OCT images. Specially, we propose a new fully convolutional deep learning architecture with a side output layer to directly learn optimal graph-edge weights from raw pixels. The architecture can automatically learn multi-scale and multi-level features to generate accurate boundary probabilities as good edge weights without hand-crafted appropriate models. The boundaries are finalized by using graph segmentation method. The proposed method is evaluated on a dataset with 130 OCT B-scans. The experimental results show the mean absolute boundary positioning differences are 1.48±0.34 pixel.