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5 March 2021 A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images
Jiaxuan Li, Yuye Ling, Jiangnan He, Peiyao Jin, Jianfeng Zhu, Haidong Zou, Xun Xu, Shuo Shao, Yu Gan, Yikai Su
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Proceedings Volume 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV; 1163010 (2021) https://doi.org/10.1117/12.2582905
Event: SPIE BiOS, 2021, Online Only
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.
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Jiaxuan Li, Yuye Ling, Jiangnan He, Peiyao Jin, Jianfeng Zhu, Haidong Zou, Xun Xu, Shuo Shao, Yu Gan, and Yikai Su "A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images", Proc. SPIE 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, 1163010 (5 March 2021); https://doi.org/10.1117/12.2582905
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KEYWORDS
Image segmentation
Optical coherence tomography
Neural networks
Computer programming
Nerve
Retina
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