Keywords: Analysis/Processing, Segmentation
Motivation: Accurate brain tumor segmentation is crucial for effective diagnosis and treatment but is often complicated by missing MRI modalities in clinical practice。
Goal(s): This study introduces a diffusion-guided multi-modal segmentation framework designed to handle missing MRI modalities, a frequent challenge in clinical tumor segmentation.
Approach: By combining a CNN-based model and a diffusion-based model, the framework adapts to incomplete data, providing robust and accurate segmentation results.
Results: Testing on the BRATS2023GLIT dataset shows that this approach outperforms conventional methods, demonstrating improved segmentation.
Impact: This approach enhances brain tumor segmentation accuracy and consistency, even with missing MRI modalities, thereby improving diagnostic precision and supporting clinical decision-making.
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