A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images

Myopic choroidal neovascularization (mCNV) is a vision-threatening complication of high myopia characterized by the growth of abnormal blood vessels in the choroid layer of the eye. In OCT images, mCNV typically presents as a highly reflective area within the subretinal layer. Therefore, accurate segmentation of mCNV in OCT images can better assist clinicians in assessing the disease status and guiding treatment decisions. However, accurate segmentation in OCT images is highly challenging due to the presence of noise interference, complex lesion areas, and low contrast. Consequently, we propose a parallel-branch network architecture that combines super token vision transformer (STViT) and CNN to more efficiently capture global dependency and low-level feature details. The super token attention mechanism (STA) in STViT reduces the number of tokens in self-attention and preserves global modeling. Additionally, we create a novel feature fusion module that utilizes depth-wise separable convolutions to efficiently fuse multi-level features from two pathways. We conduct extensive experiments on an in-house OCT dataset and a public OCT dataset, and the results demonstrate that our proposed method achieves state-of-the-art segmentation performance.