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Gliomas are one of the most prevalent types of primary brain tumours,accounting for more than 30\% of all cases and they develop from the glial stemor progenitor cells. In theory, the majority of brain tumours could well beidentified exclusively by the use of Magnetic Resonance Imaging (MRI). Each MRImodality delivers distinct information on the soft tissue of the human brainand integrating all of them would provide comprehensive data for the accuratesegmentation of the glioma, which is crucial for the patient's prognosis,diagnosis, and determining the best follow-up treatment. Unfortunately, MRI isprone to artifacts for a variety of reasons, which might result in missing oneor more MRI modalities. Various strategies have been proposed over the years tosynthesize the missing modality or compensate for the influence it has onautomated segmentation models. However, these methods usually fail to model theunderlying missing information. In this paper, we propose a style matchingU-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-trainingapproach utilizes a content and style-matching mechanism to distill theinformative features from the full-modality network into a missing modalitynetwork. To do so, we encode both full-modality and missing-modality data intoa latent space, then we decompose the representation space into a style andcontent representation. Our style matching module adaptively recalibrates therepresentation space by learning a matching function to transfer theinformative and textural features from a full-modality path into amissing-modality path. Moreover, by modelling the mutual information, ourcontent module surpasses the less informative features and re-calibrates therepresentation space based on discriminative semantic features. The evaluationprocess on the BraTS 2018 dataset shows a significant results.

SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities