  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 stem
or progenitor cells. In theory, the majority of brain tumours could well be
identified exclusively by the use of Magnetic Resonance Imaging (MRI). Each MRI
modality delivers distinct information on the soft tissue of the human brain
and integrating all of them would provide comprehensive data for the accurate
segmentation of the glioma, which is crucial for the patient's prognosis,
diagnosis, and determining the best follow-up treatment. Unfortunately, MRI is
prone to artifacts for a variety of reasons, which might result in missing one
or more MRI modalities. Various strategies have been proposed over the years to
synthesize the missing modality or compensate for the influence it has on
automated segmentation models. However, these methods usually fail to model the
underlying missing information. In this paper, we propose a style matching
U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training
approach utilizes a content and style-matching mechanism to distill the
informative features from the full-modality network into a missing modality
network. To do so, we encode both full-modality and missing-modality data into
a latent space, then we decompose the representation space into a style and
content representation. Our style matching module adaptively recalibrates the
representation space by learning a matching function to transfer the
informative and textural features from a full-modality path into a
missing-modality path. Moreover, by modelling the mutual information, our
content module surpasses the less informative features and re-calibrates the
representation space based on discriminative semantic features. The evaluation
process on the BraTS 2018 dataset shows a significant results.
