The Adaptive Hierarchical Dual Consistency framework achieved higher left atrium segmentation accuracy on cross-domain 3D LGE-CMR and CT datasets compared to other state-of-the-art semi-supervised methods.
Does the AHDC framework improve segmentation accuracy in cross-domain semi-supervised left atrium segmentation?
The proposed AHDC framework improves the accuracy of semi-supervised left atrium segmentation across different imaging domains, which may aid in the diagnosis and treatment of atrial fibrillation.
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.
Chen et al. (Fri,) conducted a other in Atrial Fibrillation (Left Atrium Segmentation) (n=418). Adaptive Hierarchical Dual Consistency (AHDC) framework vs. State-of-the-art semi-supervised methods (MT, UA-MT, DTC, Dual-Teacher) and fully supervised baseline was evaluated on Left atrium segmentation accuracy (Dice Similarity Coefficient, Jaccard Index, Average Surface Distance). The Adaptive Hierarchical Dual Consistency framework achieved higher left atrium segmentation accuracy on cross-domain 3D LGE-CMR and CT datasets compared to other state-of-the-art semi-supervised methods.