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For multi-modal magnetic resonance (MR) brain tumor image segmentation,current methods usually directly extract the discriminative features from inputimages for tumor sub-region category determination and localization. However,the impact of information aliasing caused by the mutual inclusion of tumorsub-regions is often ignored. Moreover, existing methods usually do not taketailored efforts to highlight the single tumor sub-region features. To thisend, a multi-modal MR brain tumor segmentation method with tumorprototype-driven and multi-expert integration is proposed. It could highlightthe features of each tumor sub-region under the guidance of tumor prototypes.Specifically, to obtain the prototypes with complete information, we propose amutual transmission mechanism to transfer different modal features to eachother to address the issues raised by insufficient information on single-modalfeatures. Furthermore, we devise a prototype-driven feature representation andfusion method with the learned prototypes, which implants the prototypes intotumor features and generates corresponding activation maps. With the activationmaps, the sub-region features consistent with the prototype category can behighlighted. A key information enhancement and fusion strategy withmulti-expert integration is designed to further improve the segmentationperformance. The strategy can integrate the features from different layers ofthe extra feature extraction network and the features highlighted by theprototypes. Experimental results on three competition brain tumor segmentationdatasets prove the superiority of the proposed method.

Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation