  Annotation scarcity is a long-standing problem in medical image analysis
area. To efficiently leverage limited annotations, abundant unlabeled data are
additionally exploited in semi-supervised learning, while well-established
cross-modality data are investigated in domain adaptation. In this paper, we
aim to explore the feasibility of concurrently leveraging both unlabeled data
and cross-modality data for annotation-efficient cardiac segmentation. To this
end, we propose a cutting-edge semi-supervised domain adaptation framework,
namely Dual-Teacher++. Besides directly learning from limited labeled target
domain data (e.g., CT) via a student model adopted by previous literature, we
design novel dual teacher models, including an inter-domain teacher model to
explore cross-modality priors from source domain (e.g., MR) and an intra-domain
teacher model to investigate the knowledge beneath unlabeled target domain. In
this way, the dual teacher models would transfer acquired inter- and
intra-domain knowledge to the student model for further integration and
exploitation. Moreover, to encourage reliable dual-domain knowledge transfer,
we enhance the inter-domain knowledge transfer on the samples with higher
similarity to target domain after appearance alignment, and also strengthen
intra-domain knowledge transfer of unlabeled target data with higher prediction
confidence. In this way, the student model can obtain reliable dual-domain
knowledge and yield improved performance on target domain data. We extensively
evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset.
The experiments have demonstrated the superiority of our framework over other
semi-supervised learning and domain adaptation methods. Moreover, our
performance gains could be yielded in bidirections,i.e., adapting from MR to
CT, and from CT to MR.
