  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.
