Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

• URL: http://arxiv.org/abs/2205.03525v1
• Date: Sat, 7 May 2022 02:57:50 GMT
• Title: Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
• Authors: Yuhan Xie, Kexin Jiang, Zhiyong Zhang, Shaolong Chen, Xiaodong Zhang and Changzhen Qiu
• Abstract summary: We introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling.
• Score: 2.445445375557563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-label. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.

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