RBGNet: Reliable Boundary-Guided Segmentation of Choroidal Neovascularization

Choroidal neovascularization (CNV) is a leading cause of visual impairment in retinal diseases. Optical coherence tomography angiography (OCTA) enables non-invasive CNV visualization with micrometerscale resolution, aiding precise extraction and analysis. Nevertheless, the irregular shape patterns, variable scales, and blurred lesion boundaries of CNVs present challenges for their precise segmentation in OCTA images. In this study, we propose a Reliable Boundary-Guided choroidal neovascularization segmentation Network (RBGNet) to address these issues. Specifically, our RBGNet comprises a dual-stream encoder and a multi-task decoder. The encoder consists of a convolutional neural network (CNN) stream and a transformer stream. The transformer captures global context and establishes long-range dependencies, compensating for the limitations of the CNN. The decoder is designed with multiple tasks to address specific challenges. Reliable boundary guidance is achieved by evaluating the uncertainty of each pixel label, By assigning it as a weight to regions with highly unstable boundaries, the network's ability to learn precise boundary locations can be improved, ultimately leading to more accurate segmentation results. The prediction results are also used to adaptively adjust the weighting factors between losses to guide the network's learning process. Our experimental results demonstrate that RBGNet outperforms existing methods, achieving a Dice score of $$90.42\%$$ for CNV region segmentation and $$90.25\%$$ for CNV vessel segmentation. https://github.com/iMED-Lab/RBGnet-Pytorch.git .