Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
This work addresses a key challenge in medical image analysis for computer-aided diagnosis and treatment planning, though it appears incremental as it builds on existing deep learning methods for multi-modal fusion.
The paper tackles the problem of effectively fusing information from multi-parametric MR images for 3D segmentation, proposing an uncertainty-aware feature fusion method that achieves better segmentation performance on brain tissue and abdominal multi-organ datasets compared to existing models.
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Consequently, automatic volume-of-interest segmentation based on multi-parametric MR imaging is crucial for computer-aided disease diagnosis, treatment planning, and prognosis monitoring. Despite the extensive studies conducted in deep learning-based medical image analysis, further investigations are still required to effectively exploit the information provided by different imaging parameters. How to fuse the information is a key question in this field. Here, we propose an uncertainty-aware multi-parametric MR image feature fusion method to fully exploit the information for enhanced 3D image segmentation. Uncertainties in the independent predictions of individual modalities are utilized to guide the fusion of multi-modal image features. Extensive experiments on two datasets, one for brain tissue segmentation and the other for abdominal multi-organ segmentation, have been conducted, and our proposed method achieves better segmentation performance when compared to existing models.