  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.
