@inproceedings{3c1de5b2035f48928e15bca0d6ac9a6c,
title = "A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images",
abstract = "Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other state-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively.",
keywords = "Deep learning, Glaucoma diagnosis, Global reasoning, Graph convolution, Image segmentation, Optical coherence tomography, Peripapillary retinal layer",
author = "Jiaxuan Li and Yuye Ling and Jiangnan He and Peiyao Jin and Jianfeng Zhu and Haidong Zou and Xun Xu and Shuo Shao and Yu Gan and Yikai Su",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021 ; Conference date: 06-03-2021 Through 11-03-2021",
year = "2021",
doi = "10.1117/12.2582905",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
editor = "Izatt, {Joseph A.} and Fujimoto, {James G.}",
booktitle = "Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV",
}