Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation. (1st June 2022)

• Record Type: Journal Article
• Authors: Zhang, Yuan; He, Miao; Chen, Zhineng; Hu, Kai; Li, Xuanya; Gao, Xieping
• Is Part Of: Expert systems with applications. Volume 195(2022)
• Publication Date: 2022-06-01
• Subjects: Retinal blood vessels -- Segmentation -- Context information -- Deep neural networks -- Patch-based loss weight mapping; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116526
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Highlights: We present a novel automatic method for the segmentation of retinal blood vessels. We propose Bridge-net by joint learning context-involved and non-context features. We develop a patch-based loss weight mapping to correct the imbalance of the image. We evaluate the effectiveness of the proposed method on four public datasets. The results have verified the effectiveness and stability of the proposed method.