  Multi-modality medical imaging is crucial in clinical treatment as it can
provide complementary information for medical image segmentation. However,
collecting multi-modal data in clinical is difficult due to the limitation of
the scan time and other clinical situations. As such, it is clinically
meaningful to develop an image segmentation paradigm to handle this missing
modality problem. In this paper, we propose a prototype knowledge distillation
(ProtoKD) method to tackle the challenging problem, especially for the toughest
scenario when only single modal data can be accessed. Specifically, our ProtoKD
can not only distillate the pixel-wise knowledge of multi-modality data to
single-modality data but also transfer intra-class and inter-class feature
variations, such that the student model could learn more robust feature
representation from the teacher model and inference with only one single
modality data. Our method achieves state-of-the-art performance on BraTS
benchmark. The code is available at
\url{https://github.com/SakurajimaMaiii/ProtoKD}.
