  Past few years have witnessed the artificial intelligence inspired evolution
in various medical fields. The diagnosis and treatment of gliomas -- one of the
most commonly seen brain tumors with low survival rate -- rely heavily on the
computer assisted segmentation process undertaken on the magnetic resonance
imaging (MRI) scans. Although the encoder-decoder shaped deep learning networks
have been the de facto standard style for semantic segmentation tasks in
medical imaging analysis, enormous effort is still required to be spent on
designing the detailed architecture of the down-sampling and up-sampling
blocks. In this work, we propose a neural architecture search (NAS) based
solution to brain tumor segmentation tasks on multimodal volumetric MRI scans.
Three sets of candidate operations are composed respectively for three kinds of
basic building blocks in which each operation is assigned with a specific
probabilistic parameter to be learned. Through alternately updating the weights
of operations and the other parameters in the network, the searching mechanism
ends up with two optimal structures for the upward and downward blocks.
Moreover, the developed solution also integrates normalization and patching
strategies tailored for brain MRI processing. Extensive comparative experiments
on the BraTS 2019 dataset demonstrate that the proposed algorithm not only
could relieve the pressure of fabricating block architectures but also
possesses competitive feasibility and scalability.
