  In this paper, a neural architecture search (NAS) framework is proposed for
3D medical image segmentation, to automatically optimize a neural architecture
from a large design space. Our NAS framework searches the structure of each
layer including neural connectivities and operation types in both of the
encoder and decoder. Since optimizing over a large discrete architecture space
is difficult due to high-resolution 3D medical images, a novel stochastic
sampling algorithm based on a continuous relaxation is also proposed for
scalable gradient based optimization. On the 3D medical image segmentation
tasks with a benchmark dataset, an automatically designed architecture by the
proposed NAS framework outperforms the human-designed 3D U-Net, and moreover
this optimized architecture is well suited to be transferred for different
tasks.
