DB-UNet: MLP Based Dual Branch UNet for Accurate Vessel Segmentation in OCTA Images

Optical coherence tomography angiography (OCTA) is a new non-invasive imaging technology that has been widely used in clinical practice. Automatic segmentation of retina vessels in OCTA images helps to improve the efficiency of disease diagnosis. However, due to the slender and tiny structure of retina vessels, classical deep learning segmentation methods, such as UNet and some of its variants, cannot handle it very accurately. In this work, we propose a dual branch UNet (DB-UNet), which has a pure-convolutional branch to extract detailed features such as microvessels, and a UNet branch to extract high-level features. The final output of the network is synthesized from the outputs of the two branches. We also design and apply a multilayer perceptron (MLP) block to further improve the performance of our model. Experiments on two OCTA vessel segmentation datasets show that the proposed method has better segmentation performance than existing methods.