Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention - Kuang, Xihe; Xu, Xiayu; Fang, Leyuan; Kozegar, Ehsan; Chen, Huachao; Sun, Yue; Huang, Fan; Tan, Tao (Frontiers in Medicine, 2023)
Retinal images have been proven to be significant in the diagnosis of multiple diseases such as diabetes. Retinal vessel segmentation is a crucial part for the quantitative analysis of retinal images. However, current methods mainly concentrate on the segmentation performance of overall retinal vessel structures, and the small vessels do not receive enough attention due to their small percentage in the full retinal images. Small retinal vessels are much more sensitive to the blood circulation system and have great significance in the early diagnosis and warning of various diseases. In this paper, two unsupervised methods, local phase congruency (LPC) and orientation scores (OS), were combined with a deep learning network based on the U-Net as attention. And we proposed the U-Net using local phase congruency and orientation scores (UN-LPCOS), which showed remarkable ability on the identification and segmentation of small retinal vessels. A new metric called sensitivity on small vessel (𝑺𝒆𝒔𝒗) was also proposed for the evaluation of methods’ performance on the small vessel segmentation. Our method was validated on both the DRIVE dataset and the data from Maastricht Study and achieved outstanding segmentation performance on both the overall vessel structure and small vessels.