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

• URL: http://arxiv.org/abs/2401.01654v1
• Date: Wed, 3 Jan 2024 10:22:13 GMT
• Title: LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
• Authors: Alou Diakite (1 and 2), Cheng Li (1), Lei Xie (3), Yuanjing Feng (3), Hua Han (1 and 2), Shanshan Wang (1 and 4) ( (1) Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, (2) University of Chinese Academy of Sciences, Beijing, China, (3) Zhejiang University of Technology, Hangzhou, China, (4) Peng Cheng Laboratory, Shenzhen, China)
• Abstract summary: 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. Our experiments on the human connectome project (HCP) dataset demonstrate the superior performance of our method.
• Score: 5.726588626363204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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