Cross-sequence semi-supervised learning for multi-parametric MRI-based visual pathway delineation

<i>Objective.</i>Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to the complex cross-sequence relationships, existing methods cannot effectively model the complementary information from different MRI sequences. In addition, these existing methods heavily rely on large training data with labels, which is labor-intensive and time-consuming to obtain.<i>Approach.</i>We propose a novel semi-supervised multi-parametric feature decomposition framework for VP delineation. Specifically, a correlation-constrained feature decomposition is designed to handle the complex cross-sequence relationships by capturing the unique characteristics of each MRI sequence and easing the multi-parametric information fusion process. Furthermore, a consistency-based sample enhancement module is developed to address the limited labeled data issue, by generating and promoting meaningful edge information from unlabeled data.<i>Main results.</i>We validate our framework using two public datasets and one in-house multi-shell diffusion MRI dataset. Experimental results demonstrate the superiority of our approach in terms of delineation performance when compared to six state-of-the-art approaches.<i>Significance.</i>Our proposed framework effectively mitigates the challenges of modeling complex cross-sequence relationships and limited labeled data, offering a robust solution for accurate VP delineation. This approach not only enhances the understanding of the human visual system but also holds potential for improving the diagnosis of VP-related disorders.