Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
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. In this work, we propose a novel semi-supervised multi-parametric
feature decomposition framework for VP delineation. Specifically, a
correlation-constrained feature decomposition (CFD) 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 (CSE) module is developed
to address the limited labeled data issue, by generating and promoting
meaningful edge information from unlabeled data. We validate our framework
using two public datasets, and one in-house Multi-Shell Diffusion MRI (MDM)
dataset. Experimental results demonstrate the superiority of our approach in
terms of delineation performance when compared to seven state-of-the-art
approaches.
