  Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semantic segmentation network for tumor subregion segmentation from
3D MRIs based on encoder-decoder architecture. Due to a limited training
dataset size, a variational auto-encoder branch is added to reconstruct the
input image itself in order to regularize the shared decoder and impose
additional constraints on its layers. The current approach won 1st place in the
BraTS 2018 challenge.
