Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Abstract

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.