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

TL;DR

This paper presents a novel approach combining self-supervised MAE and weak point-line supervision for meniscus segmentation in knee images, reducing labeling effort while maintaining high accuracy.

Contribution

It introduces a combined self-supervised and weakly supervised paradigm for medical image segmentation, leveraging MAE and point-line labels to improve efficiency.

Findings

Achieves near fully supervised performance with less labeled data.

Reduces labeling time significantly compared to traditional methods.

Demonstrates robustness on knee meniscus segmentation tasks.

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.