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In the field of multimodal segmentation, the correlation between differentmodalities can be considered for improving the segmentation results. In thispaper, we propose a multi-modality segmentation network with a correlationconstraint. Our network includes N model-independent encoding paths with Nimage sources, a correlation constraint block, a feature fusion block, and adecoding path. The model independent encoding path can capturemodality-specific features from the N modalities. Since there exists a strongcorrelation between different modalities, we first propose a linear correlationblock to learn the correlation between modalities, then a loss function is usedto guide the network to learn the correlated features based on the linearcorrelation block. This block forces the network to learn the latent correlatedfeatures which are more relevant for segmentation. Considering that not all thefeatures extracted from the encoders are useful for segmentation, we propose touse dual attention based fusion block to recalibrate the features along themodality and spatial paths, which can suppress less informative features andemphasize the useful ones. The fused feature representation is finallyprojected by the decoder to obtain the segmentation result. Our experimentresults tested on BraTS-2018 dataset for brain tumor segmentation demonstratethe effectiveness of our proposed method.

3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint