  An accurate and automated tissue segmentation algorithm for retinal optical
coherence tomography (OCT) images is crucial for the diagnosis of glaucoma.
However, due to the presence of the optic disc, the anatomical structure of the
peripapillary region of the retina is complicated and is challenging for
segmentation. To address this issue, we developed a novel graph convolutional
network (GCN)-assisted two-stage framework to simultaneously label the nine
retinal layers and the optic disc. Specifically, a multi-scale global reasoning
module is inserted between the encoder and decoder of a U-shape neural network
to exploit anatomical prior knowledge and perform spatial reasoning. We
conducted experiments on human peripapillary retinal OCT images. The Dice score
of the proposed segmentation network is 0.820$\pm$0.001 and the pixel accuracy
is 0.830$\pm$0.002, both of which outperform those from other state-of-the-art
techniques.
