  Deep learning has quickly become the weapon of choice for brain lesion
segmentation. However, few existing algorithms pre-configure any biological
context of their chosen segmentation tissues, and instead rely on the neural
network's optimizer to develop such associations de novo. We present a novel
method for applying deep neural networks to the problem of glioma tissue
segmentation that takes into account the structured nature of gliomas -
edematous tissue surrounding mutually-exclusive regions of enhancing and
non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net
architecture in a tree structure to create segmentations for edema,
non-enhancing tumor, and enhancing tumor regions. Specifically, training was
configured such that the whole tumor region including edema was predicted
first, and its output segmentation was fed as input into separate models to
predict enhancing and non-enhancing tumor. Our method was trained and evaluated
on the publicly available BraTS dataset, achieving Dice scores of 0.882, 0.732,
and 0.730 for whole tumor, enhancing tumor and tumor core respectively.
