STAMP: A Self-training Student-Teacher Augmentation-Driven Meta Pseudo-Labeling Framework for 3D Cardiac MRI Image Segmentation

Abstract

Medical image segmentation has significantly benefitted thanks to deep learning architectures. Furthermore, semi-supervised learning (SSL) has led to a significant improvement in overall model performance by leveraging abundant unlabeled data. Nevertheless, one shortcoming of pseudo-labeled based semi-supervised learning is pseudo-labeling bias, whose mitigation is the focus of this work. Here we propose a simple, yet effective SSL framework for image segmentation-STAMP (Student-Teacher Augmentation-driven consistency regularization via Meta Pseudo-Labeling). The proposed method uses self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data. Unlike pseudo-labeling methods, for which the Teacher network remains unchanged, meta pseudo-labeling methods allow the Teacher network to constantly adapt in response to the performance of the Student network on the labeled dataset, hence enabling the Teacher to identify more effective pseudo-labels to instruct the Student. Moreover, to improve generalization and reduce error rate, we apply both strong and weak data augmentation policies, to ensure the segmentor outputs a consistent probability distribution regardless of the augmentation level. Our extensive experimentation with varied quantities of labeled data in the training sets demonstrates the effectiveness of our model in segmenting the left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR) images. By exploiting unlabeled data with weak and strong augmentation effectively, our proposed model yielded a statistically significant 2.6% improvement (p< 0.001 ) in Dice and a 4.4% improvement (p< 0.001 ) in Jaccard over other state-of-the-art SSL methods using only 10% labeled data for training.