Evidence fusion with contextual discounting for multi-modality medical image segmentation
This addresses segmentation accuracy for brain tumor patients by improving multi-modality fusion, though it is incremental as it builds on existing evidential methods.
The paper tackled the problem of imperfect information sources in multi-modality medical image segmentation by proposing a deep framework using Dempster-Shafer theory to fuse segmentation results while accounting for modality reliability, achieving state-of-the-art performance on the BraTs 2021 database with 1251 patients.
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep framework allowing us to merge multi-MR image segmentation results using the formalism of Dempster-Shafer theory while taking into account the reliability of different modalities relative to different classes. The framework is composed of an encoder-decoder feature extraction module, an evidential segmentation module that computes a belief function at each voxel for each modality, and a multi-modality evidence fusion module, which assigns a vector of discount rates to each modality evidence and combines the discounted evidence using Dempster's rule. The whole framework is trained by minimizing a new loss function based on a discounted Dice index to increase segmentation accuracy and reliability. The method was evaluated on the BraTs 2021 database of 1251 patients with brain tumors. Quantitative and qualitative results show that our method outperforms the state of the art, and implements an effective new idea for merging multi-information within deep neural networks.