  Brain tumor is one of the most high-risk cancers which causes the 5-year
survival rate of only about 36%. Accurate diagnosis of brain tumor is critical
for the treatment planning. However, complete data are not always available in
clinical scenarios. In this paper, we propose a novel brain tumor segmentation
network to deal with the missing data issue. To compensate for missing data, we
propose to use a conditional generator to generate the missing modality under
the condition of the available modalities. As the multi-modality has a strong
correlation in tumor region, we design a correlation constraint network to
leverage the multi-source information. On the one hand, the correlation
constraint network can help the conditional generator to generate the missing
modality which should keep the multi-source correlation with the available
modalities. On the other hand, it can guide the segmentation network to learn
the correlated feature representations to improve the segmentation performance.
The proposed network consists of a conditional generator, a correlation
constraint network and a segmentation network. We carried out extensive
experiments on BraTS 2018 dataset to evaluate the proposed method.The
experimental results demonstrate the importance of the proposed components and
the superior performance of the proposed method com-pared with the
state-of-the-art methods
