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

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