Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

TL;DR

This paper introduces a hybrid semi-supervised learning framework for MRI segmentation that combines 2D and 3D models with uncertainty-guided fusion to improve accuracy on challenging data.

Contribution

The proposed HD-Teacher model uniquely integrates 2D and 3D mean-teacher networks with uncertainty-based fusion and regularization for enhanced MRI segmentation.

Findings

Outperforms existing methods on multiple MRI datasets

Effective in binary and multi-class segmentation tasks

Utilizes hybrid uncertainty to improve reliability of predictions

Abstract

Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information acquired from a single dimensionality (2D/3D), resulting in sub-optimal performance on challenging data, such as magnetic resonance imaging (MRI) scans with multiple objects and highly anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve highly effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities. This hybrid learning mechanism allows HD-Teacher to combine the `best of both worlds', utilizing features extracted from either 2D, 3D, or both dimensions to produce outputs as it sees fit. Outputs from 2D and 3D teacher models are also dynamically combined, based on their individual uncertainty scores, into a single hybrid prediction, where the hybrid uncertainty is estimated. We then propose a hybrid regularization module to encourage both student models to produce results close to the uncertainty-weighted hybrid prediction. The hybrid uncertainty suppresses unreliable knowledge in the hybrid prediction, leaving only useful information to improve network performance further. Extensive experiments of binary and multi-class segmentation conducted on three MRI datasets demonstrate the effectiveness of the proposed framework. Code is available at https://github.com/ThisGame42/Hybrid-Teacher.