Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation

TL;DR

This paper introduces a deep learning-based fusion framework for multimodal medical image segmentation that leverages Dempster-Shafer theory to quantify uncertainty and improve decision reliability in diagnoses.

Contribution

It presents a novel fusion approach combining deep learning with evidence theory and contextual discounting to enhance accuracy and reliability in multimodal medical image segmentation.

Findings

Outperforms state-of-the-art methods in accuracy.

Improves reliability of segmentation decisions.

Effective handling of modality-specific uncertainties.

Abstract

Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases based on multimodal medical images such as, e.g., PET/CT. The effective fusion of multimodal information is essential to reach a reliable decision and explain how the decision is made as well. In this paper, we propose a fusion framework for multimodal medical image segmentation based on deep learning and the Dempster-Shafer theory of evidence. In this framework, the reliability of each single modality image when segmenting different objects is taken into account by a contextual discounting operation. The discounted pieces of evidence from each modality are then combined by Dempster's rule to reach a final decision. Experimental results with a PET-CT dataset with lymphomas and a multi-MRI dataset with brain tumors show that our method outperforms the state-of-the-art methods in accuracy and reliability.