Keywords: Segmentation, Multimodal, Multi-parametric MRI, Deep Learning
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.
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