Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning

Abstract

The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to other structures in the eye. This paper proposes a new network architecture we call Segmentation-ResNet Seg-ResNet that takes a residual network structure as the main body, introduces a channel weighting structure that automatically adjusts the dependence of the feature channels, re-calibrates the feature channels, and introduces a set of low-level features that are combined with high-level features to improve network performance. Pre-fusion features and fused features are symmetrical. Hence, this work correlates with the concept of symmetry. Combined with the training strategy of migration learning, the segmentation accuracy is improved while speeding up network convergence. The robustness and effectiveness of the proposed method are demonstrated by testing data from the GlaucomaRepo and Drishti-GS fundus image databases.

Keywords

• optic cup segmentation
• residual network
• channel weighting
• feature fusion