LESEN: Label-Efficient Self-Ensembling Network for Multi-Parametric MRI-based Visual Pathway Identification

Motivation: Obtaining labeled data for visual pathway (VP) segmentation can be laborious and time-consuming. Therefore, it is crucial to develop algorithms with good performance in situations with limited labeled samples. Goal(s): The goal is to propose a label-efficient self-ensembling network (LESEN) for VP segmentation. Approach: We first introduce the LESEN model which consists of a student model and a teacher model that learn from each other using supervised and unsupervised losses. Additionally, a novel reliable unlabeled sample selection (RUSS) method is introduced to enhance the effectiveness of the LESEN model. Results: The LESEN model surpasses existing techniques on the human connectome project (HCP) dataset. Impact: The proposed LESEN model can improve visual pathway segmentation accuracy and reliability with limited labeled data, advancing multi-parametric MRI analysis in clinical and research settings.