DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation

Since the morphology of retinal vessels plays a pivotal role in clinical diagnosis of eye-related diseases and diabetic retinopathy, retinal vessels segmentation is an indispensable step for the screening and diagnosis of retinal diseases, yet it is still a challenging problem due to the complex structure of retinal vessels. Current retinal vessels segmentation approaches roughly fall into image-level and patches-level methods based on the input type, while each has its own strengths and weaknesses. To benefit from both of the input forms, we introduce a Dual Branch Transformer Module (DBTM) that can simultaneously and fully enjoy the patches-level local information and the image-level global context. Besides, the retinal vessels are long-span, thin, and distributed in strips, making the square kernel of classic convolutional neural network false as it is only suitable for most natural objects with bulk shape. To better capture context information, we further design an Adaptive Strip Upsampling Block (ASUB) to adapt to the striped distribution of the retinal vessels. Based on the above innovations, we propose a retinal vessels segmentation Network with Dual Branch Transformer and Adaptive Strip Upsampling (DA-Net). Experiments validate that our DA-Net outperforms other state-of-the-art methods on both DRIVE and CHASE-DB1 datasets.

Automated summary

The morphology of retinal vessels is crucial for the diagnosis of eye-related diseases. However, current segmentation methods have limitations. In order to make use of both local and global information and adapt to the unique distribution of retinal vessels, we propose a new network model called DA-Net.