Visual Pathway Delineation via Correlation-Constrained Feature Decomposition and Consistency-based Sample Selection

Motivation: Accurate segmentation of visual pathway (VP) in multi-parametric MRI is crucial for reliable diagnosis of visual disorders. However, existing methods face challenges due to complex multi-parametric MRI relationships and limited labeled training data. Goal(s): The goal is to improve automatic VP delineation by developing a new framework that handles complex multi-parametric MRI relationships and incorporates unlabeled data. Approach: Our framework incorporates a correlation-constrained feature decomposition module to better exploit multi-parametric MRI information and a consistency-based sample selection method for more effective semi-supervised learning. Results: Experiments on the HCP dataset show that the proposed framework achieved superior VP delineation performance compared to state-of-the-art approaches. Impact: The results of this study could have a significant impact on scientists, clinicians, and patients by improving the understanding of the human visual system and enhancing the diagnosis accuracy of visual pathway disorders.