Uncertainty-guided task-specific multi-parametric MR image fusion for brain tissue segmentation and quantification

Motivation: Existing techniques for multi-parametric MR imaging-based brain tissue segmentation typically employ a generic feature combination strategy without incorporating task-specific guidance, making it challenging to ensure effective fusion. Goal(s): In this work, we aim to develop a task-specific multi-parametric MR image fusion framework to enhance the brain tissue segmentation and quantification accuracy. Approach: During preliminary experiments, we have identified a close correlation between prediction uncertainties and prediction errors. Therefore, we propose an uncertainty-guided task-specific multi-parametric MR image fusion framework to enhance fusion efficiency and decrease prediction uncertainty. Results: Experiments on the iSeg-2019 dataset demonstrate that the proposed method achieves better results than existing techniques. Impact: The outcome of this work has the potential to be utilized in clinical practice to help physicians better monitor brain development and diagnose brain diseases. Meanwhile, the framework can be extended to diverse fields where multi-modal image fusion is required.