Abstract #0877

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

Alou Diakite^1,2, Cheng Li¹, Lei Xie³, Yuanjing Feng³, Hua Han¹, Hairong Zheng¹, and Shanshan Wang^1,4,5

¹Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Science, Beijing, China, ³Zhejiang University of Technology, Hangzhou, China, ⁴Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China, ⁵Peng Cheng Laboratory, Shenzhen, China

Synopsis

Keywords: Analysis/Processing, Brain, Semi-supervised learning

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.

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