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arXiv · 2311.09737

Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation

Bingnan Li·Zhitong Gao·Xuming He
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Abstract

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.

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Bingnan Li, Zhitong Gao, Xuming He. 2023-11-16. Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation. https://arxiv.org/abs/2311.09737

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