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.

中文翻译：

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跨域数据半监督左心房分割的自适应分层双重一致性

半监督学习在标记数据不足的左心房 (LA) 分割模型学习中具有重要意义。将半监督学习推广到跨域数据对于进一步提高模型的鲁棒性非常重要。然而，不同数据域之间广泛存在的分布差异和样本不匹配阻碍了半监督学习的推广。在这项研究中，我们通过提出一个解决这些问题用于跨域数据半监督 LA 分割的自适应分层双重一致性 (AHDC)。AHDC主要由双向对抗推理模块（BAI）和分层双重一致性学习模块（HDC）组成。BAI 克服了两个不同域之间的分布差异和样本不匹配。它主要是对抗性地学习两个映射网络，通过相互适应获得两个匹配的域。HDC 研究了基于获得的匹配域的跨域半监督分割的分层对偶学习范式。它主要构建两个双建模网络，用于挖掘域内和域间的互补信息。对于域内学习，将一致性约束应用于双建模目标以利用互补建模信息。对于域间学习，将一致性约束应用于由两个双建模网络建模的 LA，以利用不同数据域之间的互补知识。我们展示了我们提出的 AHDC 在来自不同中心的四个 3D 晚期钆增强心脏 MR (LGE-CMR) 数据集和一个 3D CT 数据集上的性能。与其他最先进的方法相比，我们提出的 AHDC 实现了更高的分割精度，这表明其在跨域半监督 LA 分割中的能力。我们展示了我们提出的 AHDC 在来自不同中心的四个 3D 晚期钆增强心脏 MR (LGE-CMR) 数据集和一个 3D CT 数据集上的性能。与其他最先进的方法相比，我们提出的 AHDC 实现了更高的分割精度，这表明其在跨域半监督 LA 分割中的能力。我们展示了我们提出的 AHDC 在来自不同中心的四个 3D 晚期钆增强心脏 MR (LGE-CMR) 数据集和一个 3D CT 数据集上的性能。与其他最先进的方法相比，我们提出的 AHDC 实现了更高的分割精度，这表明其在跨域半监督 LA 分割中的能力。