A Novel Intensity Weighting Approach Using Convolutional Neural Network for Optic Disc Segmentation in Fundus Image

Abstract

Abstract This study proposed a novel intensity weighting approach using a convolutional neural network (CNN) for fast and accurate optic disc (OD) segmentation in a fundus image. The proposed method mainly consisted of three steps involving CNN-based importance calculation of pixel, image reconstruction, and OD segmentation. In the first step, the CNN model composed of four convolution and pooling layers was designed and trained. Then, the heat map was generated by applying a gradient-weighted class activation map algorithm to the final convolution layer of the model. In the next step, each of the pixels on the image was assigned a weight based on the previously obtained heat map. In addition, the retinal vessel that may interfere with OD segmentation was detected and substituted based on the nearest neighbor pixels. Finally, the OD region was segmented using Otsu’s method. As a result, the proposed method achieved a high segmentation accuracy of 98.61%, which was improved about 4.61% than the result without the weight assignment.