Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
This work addresses the challenge of efficient and accurate meniscus segmentation in knee joint images for medical applications, representing an incremental improvement by combining existing techniques.
The paper tackles the problem of insufficient datasets and time-consuming labeling in medical image segmentation by introducing a self-supervised MAE method and a point-line weak supervision paradigm for meniscus segmentation, achieving performance nearly equal to fully supervised models while reducing labeling time and dataset size.
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.