Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality
  MRI Segmentation
Bingnan Li Zhitong Gao Xuming He
Cross-modal MRI segmentation is of great value for computer-aided medical
diagnosis, enabling flexible data acquisition and model generalization.
However, most existing methods have difficulty in handling local variations in
domain shift and typically require a significant amount of data for training,
which hinders their usage in practice. To address these problems, we propose a
novel adaptive domain generalization framework, which integrates a
learning-free cross-domain representation based on image gradient maps and a
class prior-informed test-time adaptation strategy for mitigating local domain
shift. We validate our approach on two multi-modal MRI datasets with six
cross-modal segmentation tasks. Across all the task settings, our method
consistently outperforms competing approaches and shows a stable performance
even with limited training data.