Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation

TL;DR

This paper introduces an uncertainty-aware fusion method for multi-parametric MRI that leverages prediction uncertainties to improve 3D segmentation accuracy in medical imaging.

Contribution

It presents a novel uncertainty-guided feature fusion approach for multi-parametric MRI, enhancing segmentation performance over existing methods.

Findings

Improved segmentation accuracy on brain tissue datasets.

Enhanced multi-organ segmentation results.

Effective utilization of uncertainty in multi-modal data fusion.

Abstract

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.