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Recent research has shown the potential of deep learning in multi-parametricMRI-based visual pathway (VP) segmentation. However, obtaining labeled data fortraining is laborious and time-consuming. Therefore, it is crucial to developeffective algorithms in situations with limited labeled samples. In this work,we propose a label-efficient deep learning method with self-ensembling (LESEN).LESEN incorporates supervised and unsupervised losses, enabling the student andteacher models to mutually learn from each other, forming a self-ensemblingmean teacher framework. Additionally, we introduce a reliable unlabeled sampleselection (RUSS) mechanism to further enhance LESEN's effectiveness. Ourexperiments on the human connectome project (HCP) dataset demonstrate thesuperior performance of our method when compared to state-of-the-arttechniques, advancing multimodal VP segmentation for comprehensive analysis inclinical and research settings. The implementation code will be available at:https://github.com/aldiak/Semi-Supervised-Multimodal-Visual-Pathway-Delineation.

LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation