  Semi-supervised learning provides great significance in left atrium (LA)
segmentation model learning with insufficient labelled data. Generalising
semi-supervised learning to cross-domain data is of high importance to further
improve model robustness. However, the widely existing distribution difference
and sample mismatch between different data domains hinder the generalisation of
semi-supervised learning. In this study, we alleviate these problems by
proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the
semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists
of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual
Consistency learning module (HDC). The BAI overcomes the difference of
distributions and the sample mismatch between two different domains. It mainly
learns two mapping networks adversarially to obtain two matched domains through
mutual adaptation. The HDC investigates a hierarchical dual learning paradigm
for cross-domain semi-supervised segmentation based on the obtained matched
domains. It mainly builds two dual-modelling networks for mining the
complementary information in both intra-domain and inter-domain. For the
intra-domain learning, a consistency constraint is applied to the
dual-modelling targets to exploit the complementary modelling information. For
the inter-domain learning, a consistency constraint is applied to the LAs
modelled by two dual-modelling networks to exploit the complementary knowledge
among different data domains. We demonstrated the performance of our proposed
AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from
different centres and a 3D CT dataset. Compared to other state-of-the-art
methods, our proposed AHDC achieved higher segmentation accuracy, which
indicated its capability in the cross-domain semi-supervised LA segmentation.
