Optic disc segmentation by U-net and probability bubble in abnormal fundus images

Abstract

Segmenting optic disc (OD) in abnormal fundus images is a challenge task because of many distractions such as illumination variations, blurry boundary, occlusion of retinal vessels and big bright lesions. Data-driven deep learning is effective and robust to illumination variations, blurry boundary and occlusion in the normal fundus images but sensitive to big bright lesions in abnormal images. In this paper, an automatic OD segmentation method fusing U-net with model-driven probability bubble approach is proposed in abnormal fundus images. The probability bubble is conceived according to the position relationship between retinal vessels and OD, and the localization result is fused into the output layer of U-net through calculating the joint probability. The proposed method takes the advantage of the deep learning architecture and improves the architecture’s performance by including the model-driven position constraint when lack of sufficient training data. Experiments show that the proposed method successfully removes the distraction of bright lesions in abnormal fundus images and obtains a satisfying OD segmentation on three public databases: Kaggle, MESSIDOR and NIVE, and it outperforms existing methods with a very high accuracy.