Deep evidential fusion with uncertainty quantification and contextual
  discounting for multimodal medical image segmentation
Ling Huang Su Ruan Pierre Decazes Thierry Denoeux
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.