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Abstract #80913 Published in IGR 20-3
JointRCNN: A Region-based Convolutional Neural Network for Optic Disc and Cup Segmentation
Jiang Y; Duan L; Cheng J; Gu Z; Xia H; Fu H; Li C; Liu JIEEE Transactions on Bio-Medical Engineering 2020; 67: 335-343
OBJECTIVE: The purpose of this paper is to propose a novel algorithm for joint optic disc and cup segmentation, which aids the glaucoma detection. METHODS: By assuming the shapes of cup and disc regions are elliptical, we proposed an end-to-end region-based convolutional neural network for joint optic disc and cup segmentation (referred to as Joint RCNN). Atrous convolution is introduced to boost the performance of feature extraction module. In JointRCNN, disc proposal network (DPN) and cup proposal network (CPN) are proposed to generate bounding box proposals for optic disc and cup respectively. Given the prior knowledge that the optic cup is located in the optic disc, disc attention module is proposed to connect DPN and CPN, where a suitable bounding box of the optic disc is first selected and then continued to be forward propagated as the basis for optic cup detection in our proposed network. After obtaining the disc and cup regions which are the inscribed ellipses of the corresponding detected bounding boxes, the vertical cup-to-disc ratio (CDR) is computed and used as an indicator for glaucoma detection. RESULTS: Comprehensive experiments clearly show that our JointRCNN model outperforms state-of-the-art methods for optic disc and cup segmentation task and glaucoma detection task. CONCLUSION: Joint optic disc and cup segmentation, which utilizes the connection between optic disc and cup, could improve the performance of optic disc and cup segmentation. SIGNIFICANCE: The proposed method improves the accuracy of glaucoma detection. It is promising to be used for glaucoma screening.
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Classification:
15 Miscellaneous
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
