A Multichannel Deep Neural Network for Retina Vessel Segmentation via a Fusion Mechanism - Ding, Jiaqi; Zhang, Zehua; Tang, Jijun; Guo, Fei (Frontiers in Bioengineering and Biotechnology, 2021)
Changes in fundus blood vessels reflect the occurrence of eye diseases, and from this we can explore other physical diseases that cause fundus lesions, such as diabetes and hypertension complication. However, the existing computational methods lack high efficiency and precision segmentation for the vascular ends and thin retina vessels. It is important to construct a reliable and quantitative automatic diagnostic method for improving diagnosis efficiency. In this study, we propose multi-channel deep neural network for retina vessel segmentation. First, we apply U-net on original and thin(or thick) vessels for multi-objective optimization for purposively training thick and thin vessels. Then, we design a specific fusion mechanism for combining three kinds of prediction probability maps into final binary segmentation map. Experiments show that our method can effectively improve the segmentation performances of thin blood vessels and vascular ends. It outperforms the many current excellent vessel segmentation methods on three public datasets. In particular, it is pretty impressive that we achieve best F1-score with 0.8201 on the DRIVE dataset and 0.8239 on the STARE dataset. The findings of this study have the potential for application in automated retinal image analysis, and it may provide a new, general and high-performance computing framework for image segmentation.