Uncertainty-generation-based diffusion probability model for brain tumor segmentation

Brain tumor is one of the most dangerous diseases. Automated brain tumor segmentation technology is particularly important in the diagnosis and treatment of brain tumors. Traditional brain tumor segmentation methods mostly rely on UNet or associate variants, and the segmentation performance is highly dependent on the feature extraction quality. Recently, diffusion probabilistic model (DPM) has received a lot of attention and achieved remarkable success in medical image segmentation. However, the existing DPM-based brain tumor segmentation method did not utilize the advantages of complementary information between multimodal MRI. Additionally, they all constrained the generation of DPM using the original images. In this work, we propose a DPM-based brain tumor segmentation method, which consists of DPM, uncertainty generation module and collaborative Module. The collaborative module takes the input MRI from multimodal information and dynamically provide conditional constraints for DPM. This allows DPM to obtain more detailed brain tumor features. Considering that Previous works mainly ignore the influence of DPM's uncertainty on the results, we proposed an uncertainty generation module. It calculates the uncertainty of each step of the DPM and assigns corresponding uncertainty weights. The results of each step are fused according to inferred uncertainty weights to get the final segmentations. The proposed method obtained 89.32% and 87.82% dice scores on the BraTS2020 and BraTS2021 datasets, respectively, which verified the effectiveness of the proposed method.