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Hierarchical Disentanglement Guided Diffusion for Multimodal Brain Tumor Segmentation
IEEE Journal of Biomedical and Health Informatics
|August 6, 2026
Summary
This study introduces HD-Diff, a novel framework for multimodal brain tumor segmentation using diffusion models. It improves accuracy by harmonizing multimodal features and precisely incorporating tumor structure information.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Brain tumor segmentation from MRI is vital for clinical decisions.
- Existing diffusion models struggle with multimodal data integration and structural information.
- Challenges include local modality differences and global tumor heterogeneity.
Purpose of the Study:
- To develop an advanced framework for multimodal brain tumor segmentation.
- To address limitations in current diffusion-based segmentation methods.
- To enhance the accuracy of tumor boundary and core region identification.
Main Methods:
- Proposed a hierarchical disentanglement guided diffusion framework (HD-Diff).
- Introduced a modality-aware encoder for feature harmonization and integration.
- Employed a dual-stream feature fusion module to resolve inter-modality inconsistencies.
- Utilized boundary and core enhanced conditioners for hierarchical feature injection into the denoising process.
Main Results:
- HD-Diff demonstrated strong overall segmentation performance across multiple public datasets.
- Achieved highly competitive Dice and HD95 scores, indicating superior accuracy.
- Effectiveness was consistent across diverse brain tumor types and imaging data.
Conclusions:
- HD-Diff effectively addresses multimodal feature integration and structural information challenges in brain tumor segmentation.
- The proposed hierarchical approach enables independent optimization of tumor topology and edge details.
- This framework offers a promising advancement for automated and accurate brain tumor segmentation.