LESEN: Label-Efficient deep learning for Multi-parametric MRI-based
  Visual Pathway Segmentation
Alou Diakite Cheng Li Lei Xie Yuanjing Feng Hua Han Shanshan Wang
Recent research has shown the potential of deep learning in multi-parametric
MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for
training is laborious and time-consuming. Therefore, it is crucial to develop
effective 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 and
teacher models to mutually learn from each other, forming a self-ensembling
mean teacher framework. Additionally, we introduce a reliable unlabeled sample
selection (RUSS) mechanism to further enhance LESEN's effectiveness. Our
experiments on the human connectome project (HCP) dataset demonstrate the
superior performance of our method when compared to state-of-the-art
techniques, advancing multimodal VP segmentation for comprehensive analysis in
clinical and research settings. The implementation code will be available at:
https://github.com/aldiak/Semi-Supervised-Multimodal-Visual-Pathway-
Delineation.