Attention-Enhanced Disentangled Representation Learning for Unsupervised Domain Adaptation in Cardiac Segmentation

To overcome the barriers of multimodality and scarcity of annotations in medical image segmentation, many unsupervised domain adaptation (UDA) methods have been proposed, especially in cardiac segmentation. However, these methods may not completely avoid the interference of domain-specific information. To tackle this problem, we propose a novel Attention-enhanced Disentangled Representation (ADR) learning model for UDA in cardiac segmentation. To sufficiently remove domain shift and mine more precise domain-invariant features, we first put forward a strategy from image-level coarse alignment to fine removal of remaining domain shift. Unlike previous dual path disentanglement methods, we present channel-wise disentangled representation learning to promote mutual guidance between domain-invariant and domain-specific features. Meanwhile, Hilbert-Schmidt independence criterion (HSIC) is adopted to establish the independence between the disentangled features. Furthermore, we propose an attention bias for adversarial learning in the output space to enhance the learning of task-relevant domain-invariant features. To obtain more accurate predictions during inference, an information fusion calibration (IFC) is also proposed. Extensive experiments on the MMWHS 2017 dataset demonstrate the superiority of our method. Code is available at https://github.com/Sunxy11/ADR.