Multimodal knowledge distillation-guided artery-vein segmentation network for OCT/OCTA images

The analysis of artery and vein differences in Optical Coherence Tomography Angiography (OCTA) is of great significance for diagnosing various eye diseases and systemic diseases (such as diabetic retinopathy, hypertension, and cardiovascular diseases). We propose a multimodal knowledge distillation-guided Artery-Vein Segmentation Network for OCT/OCTA images (MKD-Net). Firstly, we introduce 2D projection image information in the process of segmenting from 3D to 2D to enhance the semantic representation capability in the horizontal direction of the network. Secondly, we transfer knowledge from a trained multimodal (3D and 2D input) teacher network to a single-modal (3D input) student network, enabling the student network to not rely on the projection images generated by retinal layer segmentation, avoiding segmentation failure under diseased conditions, and enhancing its ability to capture details and feature changes in the images. Extensive evaluations on the OCTA500 and OCTA100 datasets demonstrate that the proposed method achieves superior performance, with an MIou of 84.96% and 84.25% on the OCTA500 and OCTA100 datasets respectively.