  Brain tumor segmentation based on multi-modal magnetic resonance imaging
(MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and
postoperative evaluations. Despite the achieved inspiring performance by
existing automatic segmentation methods, multi-modal MRI data are still
unavailable in real-world clinical applications due to quite a few
uncontrollable factors (e.g. different imaging protocols, data corruption, and
patient condition limitations), which lead to a large performance drop during
practical applications. In this work, we propose a Deeply supervIsed knowledGE
tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation
under different modality-missing scenarios. Specifically, a knowledge transfer
learning frame is constructed, enabling a student model to learn
modality-shared semantic information from a teacher model pretrained with the
complete multi-modal MRI data. To simulate all the possible modality-missing
conditions under the given multi-modal data, we generate incomplete multi-modal
MRI samples based on Bernoulli sampling. Finally, a deeply supervised knowledge
transfer loss is designed to ensure the consistency of the teacher-student
structure at different decoding stages, which helps the extraction of inherent
and effective modality representations. Experiments on the BraTS 2020 dataset
demonstrate that our method achieves promising results for the incomplete
multi-modal MR image segmentation task.
