Vision Transformer-Based Retina Vessel Segmentation with Deep Adaptive Gamma Correction

Accurate segmentation of the retina vessel is essential for the early diagnosis of eye-related diseases. Recently, convolutional neural networks have shown remarkable performance in retina vessel segmentation. However, the complexity of edge structural information and the changeable intensity distribution depending on retina images reduce the performance of the segmentation tasks. This paper proposes two novel deep learning-based modules, channel attention vision transformer (CAViT) and deep adaptive gamma correction (DAGC), to tackle these issues. The CAViT jointly applies the efficient channel attention (ECA) and the vision transformer (ViT), in which the channel attention module considers the interdependency among feature channels and the ViT discriminates meaningful edge structures by considering the global context. The DAGC module provides the optimal gamma correction value for each input image by jointly training a CNN model with the segmentation network so that all the retina images are mapped to a unified intensity distribution. The experimental results show that our proposed method achieves superior performance compared to conventional methods on widely used datasets, DRIVE and CHASE DB1.