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.
@article{arxiv.2205.03525,
title = {Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm},
author = {Yuhan Xie and Kexin Jiang and Zhiyong Zhang and Shaolong Chen and Xiaodong Zhang and Changzhen Qiu},
journal= {arXiv preprint arXiv:2205.03525},
year = {2022}
}