SVS-net: A Novel Semantic Segmentation Network in Optical Coherence Tomography Angiography Images
This addresses the challenge of accurate retinal microvasculature segmentation for neuroretinal and systemic disease analysis, but is incremental as it builds on existing segmentation methods with a new attention module.
The study tackled the problem of speckle noise artifacts in optical coherence tomography angiography (OCTA) images for automated vascular segmentation, proposing SVS-net which achieved better performance in accuracy, recall, F1 score, and Kappa score compared to other models.
Automated vascular segmentation on optical coherence tomography angiography (OCTA) is important for the quantitative analyses of retinal microvasculature in neuroretinal and systemic diseases. Despite recent improvements, artifacts continue to pose challenges in segmentation. Our study focused on removing the speckle noise artifact from OCTA images when performing segmentation. Speckle noise is common in OCTA and is particularly prominent over large non-perfusion areas. It may interfere with the proper assessment of retinal vasculature. In this study, we proposed a novel Supervision Vessel Segmentation network (SVS-net) to detect vessels of different sizes. The SVS-net includes a new attention-based module to describe vessel positions and facilitate the understanding of the network learning process. The model is efficient and explainable and could be utilized to reduce the need for manual labeling. Our SVS-net had better performance in accuracy, recall, F1 score, and Kappa score when compared to other well recognized models.