LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
This work addresses the challenge of limited labeled data for medical image segmentation, benefiting clinical and research settings, but it is incremental as it builds on existing semi-supervised and self-ensembling approaches.
The paper tackles the problem of labor-intensive labeling for multi-parametric MRI-based visual pathway segmentation by proposing LESEN, a label-efficient deep learning method with self-ensembling, which outperforms state-of-the-art techniques on the HCP dataset.
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.