Diff-CFFBNet: Diffusion-Embedded Cross-Layer Feature Fusion Bridge Network for Brain Tumor Segmentation

This study introduces the Diff-CFFBNet, a novel network for brain tumor segmentation designed to address the challenges of misdetection in broken tumor regions within MRI scans, which is crucial for early diagnosis, treatment planning, and disease monitoring. The proposed method incorporates a cross-layer feature fusion bridge (CFFB) to enhance feature interaction and a cross-layer feature fusion U-Net (CFFU-Net) to reduce the semantic gap in diffusion models. Additionally, a sampling-quantity-based fusion (SQ-Fusion) is utilized to leverage the uncertainty of diffusion models for improved segmentation outcomes. Experimental validation on BraTS 2019, BraTS 2020, TCGA-GBM, TCGA-LGG, and MSD datasets demonstrates that Diff-CFFBNet outperforms existing methods, achieving superior performance in terms of Dice score, HD95, and mIoU metrics. These results indicate the model's robustness and precision, even under challenging conditions with complex tumor structures. Diff-CFFBNet provides a reliable solution for accurate and efficient brain tumor segmentation in medical imaging, with the potential for clinical application in treatment planning and disease monitoring. Future work aims to extend this approach to multiple tumor types and refine diffusion model applications in medical image segmentation.