Weakly supervised semantic segmentation for optic disc of fundus image

Abstract

We propose a weakly supervised learning algorithm with size constraints based on modified deep convolutional neural networks (CNN) to segment the optic disc in fundus images. Comparing with the existing fully supervised method, we only use image-level labels and bounding box labels to guide segmentation. To obtain a more accurate coarse foreground segmentation map with image-level labels and treat them as “GroundTruth” for the next training stage, we combine the improved constraint CNN method and GrabCut method to generate the coarse foreground segmentation map. Then we design a weak loss function to constrain the output size and training network base on a modified U-net model with the generated foreground segmentation map. The proposed algorithm demonstrates state-of-the-art results on RIM-ONE database and DRISHTI-GS database.