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.

在视力异常的眼底图像中分割视盘（OD）是一项艰巨的任务，因为存在许多干扰因素，例如光照变化，边界模糊，视网膜血管阻塞和大的明亮病变。数据驱动的深度学习对正常眼底图像中的光照变化，边界模糊和遮挡是有效且强大的，但对异常图像中的大的明亮病变敏感。本文提出了一种将U-net与模型驱动概率气泡法融合的自动OD分割方法。根据视网膜血管与OD之间的位置关系构想概率气泡，并通过计算联合概率将定位结果融合到U-net的输出层中。所提出的方法利用了深度学习架构的优势，并在缺少足够的训练数据时通过包含模型驱动的位置约束来提高架构的性能。实验表明，该方法成功去除了异常眼底图像中明亮病变的干扰，并在Kaggle，MESSIDOR和NIVE这三个公共数据库上获得了令人满意的OD分割，并且以非常高的精度优于现有方法。