BUFNet: Boundary-aware and uncertainty-driven multi-modal fusion network for MR brain tumor segmentation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
• Existing methods struggle with complex tumor boundaries and lack uncertainty estimation.
• Deterministic segmentation results limit clinical reliability and interpretability.

Purpose of the Study:

• To propose a novel Boundary-aware and Uncertainty-driven multi-modal Fusion Network (BUFNet) for enhanced MR brain tumor segmentation.
• To address challenges in segmenting complex tumor boundaries and quantifying prediction uncertainty.
• To improve the reliability and interpretability of segmentation results in clinical settings.

Main Methods:

• Developed a boundary-aware mechanism to extract and utilize tumor boundary information for improved discrimination.
• Implemented an effective multi-modal fusion strategy to integrate complementary information from various MR modalities.
• Introduced an uncertainty-based segmentation loss function and incorporated uncertainty quantification for confidence measures.

Main Results:

• The proposed BUFNet demonstrated superior performance compared to existing state-of-the-art methods.
• The boundary-aware mechanism effectively improved the discrimination of tumor sub-regions.
• Uncertainty quantification provided valuable confidence measures for clinical interpretation.

Conclusions:

• BUFNet offers a significant advancement in MR brain tumor segmentation by effectively handling complex boundaries and uncertainty.
• The integration of boundary awareness and uncertainty-driven approaches enhances segmentation accuracy and clinical utility.
• The method shows promise for improving diagnostic accuracy and treatment planning in neuro-oncology.