  Deep learning algorithms, in particular 2D and 3D fully convolutional neural
networks (FCNs), have rapidly become the mainstream methodology for volumetric
medical image segmentation. However, 2D convolutions cannot fully leverage the
rich spatial information along the third axis, while 3D convolutions suffer
from the demanding computation and high GPU memory consumption. In this paper,
we propose to automatically search the network architecture tailoring to
volumetric medical image segmentation problem. Concretely, we formulate the
structure learning as differentiable neural architecture search, and let the
network itself choose between 2D, 3D or Pseudo-3D (P3D) convolutions at each
layer. We evaluate our method on 3 public datasets, i.e., the NIH Pancreas
dataset, the Lung and Pancreas dataset from the Medical Segmentation Decathlon
(MSD) Challenge. Our method, named V-NAS, consistently outperforms other
state-of-the-arts on the segmentation task of both normal organ (NIH Pancreas)
and abnormal organs (MSD Lung tumors and MSD Pancreas tumors), which shows the
power of chosen architecture. Moreover, the searched architecture on one
dataset can be well generalized to other datasets, which demonstrates the
robustness and practical use of our proposed method.
