Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor
  Image Segmentation
Yafei Zhang Zhiyuan Li Huafeng Li Dapeng Tao
For multi-modal magnetic resonance (MR) brain tumor image segmentation,
current methods usually directly extract the discriminative features from input
images for tumor sub-region category determination and localization. However,
the impact of information aliasing caused by the mutual inclusion of tumor
sub-regions is often ignored. Moreover, existing methods usually do not take
tailored efforts to highlight the single tumor sub-region features. To this
end, a multi-modal MR brain tumor segmentation method with tumor
prototype-driven and multi-expert integration is proposed. It could highlight
the features of each tumor sub-region under the guidance of tumor prototypes.
Specifically, to obtain the prototypes with complete information, we propose a
mutual transmission mechanism to transfer different modal features to each
other to address the issues raised by insufficient information on single-modal
features. Furthermore, we devise a prototype-driven feature representation and
fusion method with the learned prototypes, which implants the prototypes into
tumor features and generates corresponding activation maps. With the activation
maps, the sub-region features consistent with the prototype category can be
highlighted. A key information enhancement and fusion strategy with
multi-expert integration is designed to further improve the segmentation
performance. The strategy can integrate the features from different layers of
the extra feature extraction network and the features highlighted by the
prototypes. Experimental results on three competition brain tumor segmentation
datasets prove the superiority of the proposed method.