  Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical
technique for early diagnosis. However, rather than having complete four
modalities as in BraTS dataset, it is common to have missing modalities in
clinical scenarios. We design a brain tumor segmentation algorithm that is
robust to the absence of any modality. Our network includes a
channel-independent encoding path and a feature-fusion decoding path. We use
self-supervised training through channel dropout and also propose a novel
domain adaptation method on feature maps to recover the information from the
missing channel. Our results demonstrate that the quality of the segmentation
depends on which modality is missing. Furthermore, we also discuss and
visualize the contribution of each modality to the segmentation results. Their
contributions are along well with the expert screening routine.
