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

Alou Diakite^1,2, Cheng Li¹, Lei Xie³

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

Physics in Medicine and Biology

|December 17, 2025

Summary

This summary is machine-generated.

This study introduces a new semi-supervised framework for precise visual pathway (VP) delineation using multi-parametric MRI. The method effectively models complex MRI data relationships and reduces reliance on labeled data for improved diagnostic accuracy.

Keywords:

feature decomposition multi-parametric MRI semi-supervised learning visual pathway

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Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Accurate delineation of the visual pathway (VP) is essential for understanding visual system function and diagnosing related pathologies.
Multi-parametric MRI data offers rich information but presents challenges in modeling cross-sequence relationships and requires extensive labeled data for training.
Existing methods struggle to effectively integrate complementary information from diverse MRI sequences and are limited by the need for large annotated datasets.

Purpose of the Study:

To develop a novel semi-supervised framework for accurate visual pathway (VP) delineation that overcomes limitations of existing methods.
To effectively model complex cross-sequence relationships within multi-parametric MRI data.
To address the challenge of limited labeled training data in medical image segmentation tasks.

Main Methods:

A semi-supervised multi-parametric feature decomposition framework integrating a correlation-constrained feature decomposition (CFD) module.
The CFD module captures unique MRI sequence characteristics and facilitates information fusion.
A consistency-based sample enhancement (CSE) module leverages unlabeled data to generate and reinforce edge information, mitigating the need for extensive labels.

Main Results:

The proposed framework was validated on two public datasets and one in-house Multi-Shell Diffusion MRI (MDM) dataset.
Experimental results demonstrated superior delineation performance compared to six state-of-the-art approaches.
The framework effectively handled complex cross-sequence relationships and limited labeled data scenarios.

Conclusions:

The developed framework provides a robust solution for accurate visual pathway (VP) delineation, addressing key challenges in multi-parametric MRI analysis.
This approach enhances the understanding of the human visual system.
The method holds significant potential for improving the diagnosis of visual pathway-related disorders.