Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation

TL;DR

This paper introduces UC-Seg, an uncertainty-aware cross-training framework for semi-supervised medical image segmentation that uses dual subnets and uncertainty-guided pseudo-labeling to improve accuracy and robustness across various imaging modalities.

Contribution

The paper proposes a novel dual-subnet framework with cross-consistency and uncertainty-aware pseudo-labeling to mitigate biases and leverage unlabeled data effectively in semi-supervised segmentation.

Findings

Achieves superior segmentation accuracy over state-of-the-art methods.

Demonstrates robustness across multiple medical imaging modalities.

Improves generalization performance in semi-supervised settings.

Abstract

Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mean-teacher (MT) based semi-supervised methods utilize consistency regularization to effectively leverage valuable information from unlabeled data. However, these methods often heavily rely on the student model and overlook the potential impact of cognitive biases within the model. Furthermore, some methods employ co-training using pseudo-labels derived from different inputs, yet generating high-confidence pseudo-labels from perturbed inputs during training remains a significant challenge. In this paper, we propose an Uncertainty-aware Cross-training framework for semi-supervised medical image Segmentation (UC-Seg). Our UC-Seg framework incorporates two distinct subnets to effectively explore and leverage the correlation between them, thereby mitigating cognitive biases within the model. Specifically, we present a Cross-subnet Consistency Preservation (CCP) strategy to enhance feature representation capability and ensure feature consistency across the two subnets. This strategy enables each subnet to correct its own biases and learn shared semantics from both labeled and unlabeled data. Additionally, we propose an Uncertainty-aware Pseudo-label Generation (UPG) component that leverages segmentation results and corresponding uncertainty maps from both subnets to generate high-confidence pseudo-labels. We extensively evaluate the proposed UC-Seg on various medical image segmentation tasks involving different modality images, such as MRI, CT, ultrasound, colonoscopy, and so on. The results demonstrate that our method achieves superior segmentation accuracy and generalization performance compared to other state-of-the-art semi-supervised methods. Our code will be released at https://github.com/taozh2017/UCSeg.