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

• URL: http://arxiv.org/abs/2311.09737v1
• Date: Thu, 16 Nov 2023 10:07:27 GMT
• Title: Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation
• Authors: Bingnan Li, Zhitong Gao, Xuming He
• Abstract summary: Cross-modal MRI segmentation is of great value for computer-aided medical diagnosis, enabling flexible data acquisition and model generalization. Most existing methods have difficulty in handling local variations in domain shift and typically require a significant amount of data for training. We propose a novel adaptive domain generalization framework, which integrates a learning-free cross-domain representation based on image gradient maps.
• Score: 14.209197648189203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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