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• W3046114444 abstract "Accurate optic cup and optic disc (OC, OD) segmentation is the prerequisite for cup-disc ratio (CDR) calculation. In this paper, a new full convolutional neural network (FCN) with multi-scale residual module is proposed. Firstly, polar coordinate transformation was introduced to balance the CDR with space constraints, and CLAHE was implemented in fundus images for contrast enhancement. Secondly, W-Net-R model was proposed as the main framework, while the standard convolution unit was replaced by the multi-scale residual module. Finally, the multi-label cost function is utilized to guide its functioning. In the experiment, the REFUGE dataset was used for training, validation and testing. We obtained 0.979 and 0.904 for OD and OC segmentations on MIoU, which indicates a relative improvement of 4.04% and 3.55%, comparing with that of U-Net, respectively. Experiment results proved that our proposed method is superior to other state-of-the-art schemes on OC and OD segmentation, and could be a potential prospective tool for early screening of glaucoma." @default.
• W3046114444 created "2020-08-03" @default.
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• W3046114444 date "2020-11-01" @default.
• W3046114444 modified "2023-10-18" @default.
• W3046114444 title "Full Convolutional Neural Network with Multi-Scale Residual Model for Optic Cup and Disc Segmentation" @default.
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• W3046114444 doi "https://doi.org/10.1166/jmihi.2020.3208" @default.
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