  Automatic brain tumor segmentation method plays an extremely important role
in the whole process of brain tumor diagnosis and treatment. In this paper, we
propose a multi-step cascaded network which takes the hierarchical topology of
the brain tumor substructures into consideration and segments the substructures
from coarse to fine .During segmentation, the result of the former step is
utilized as the prior information for the next step to guide the finer
segmentation process. The whole network is trained in an end-to-end fashion.
Besides, to alleviate the gradient vanishing issue and reduce overfitting, we
added several auxiliary outputs as a kind of deep supervision for each step and
introduced several data augmentation strategies, respectively, which proved to
be quite efficient for brain tumor segmentation. Lastly, focal loss is utilized
to solve the problem of remarkably imbalance of the tumor regions and
background. Our model is tested on the BraTS 2019 validation dataset, the
preliminary results of mean dice coefficients are 0.886, 0.813, 0.771 for the
whole tumor, tumor core and enhancing tumor respectively. Code is available at
https://github.com/JohnleeHIT/Brats2019
