Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

TL;DR

This paper introduces an uncertainty-aware semi-supervised learning framework that improves 3D left atrium segmentation from MR images by leveraging unlabeled data through a student-teacher model with uncertainty estimation.

Contribution

It proposes a novel uncertainty-aware semi-supervised approach with a student-teacher model for medical image segmentation, enhancing performance by exploiting unlabeled data.

Findings

Achieves significant performance improvements over existing methods.

Outperforms state-of-the-art semi-supervised segmentation techniques.

Effectively leverages unlabeled data with uncertainty estimation.

Abstract

Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a student model and a teacher model, and the student model learns from the teacher model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher model. We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems.