Focused MRI Segmentation: Leveraging Diffusion Models for Brain Tumor Segmentation in Low-Resolution Areas

Motivation: Glioblastoma, the most prevalent and aggressive adult brain tumor, presents a therapeutic and monitoring challenge due to its diverse morphology and composition. Goal(s): This study explores the efficacy of utilizing denoising diffusion models with the widely adopted U-Net architecture for enhanced segmentation performance. Approach: The proposed framework notably improves the segmentation of the tumor, especially the core. This enhancement facilitates an advanced understanding of complex cases and potentially impacts specialist interventions. Results: Our findings present promising results for further research into more intricate glioblastoma cases, thereby aiding in developing sophisticated, targeted treatment strategies for this disease. Impact: This study advances U-Net architecture by integrating denoising diffusion models and specialized loss functions, elevating the precision of low-resolution brain tumor segmentation, with an emphasis on balancing improved accuracy against uncertain predictions.