Automatic Localization of Optic Disc Based on Deep Learning in Fundus Images

The optic disc (OD) contains lots of important information in retinal image analysis. Detecting the region of OD correctly is important for subsequent analysis of retinal images. It is challenging to locate the OD precisely due to the various reasons including low image quality or lesions around the OD. In this paper, we propose a cascading localization method based on deep learning with feedback to improve the accuracy of OD localization. The method employs the model of saliency-based visual attention to find the most salient region and implements deep convolution neural network (CNN) to determine whether it contains OD. If a region is classified as non-OD region, we find the next salient region and input it into the CNN to classify. The algorithm ends when the CNN finds a region with OD. The proposed method is evaluated on the ORIGA and MESSIDOR datasets. Our experimental results show that the proposed method achieved significant improvement in detection of OD compared with previous methods.