  Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolution and residual connection. With these essential building
blocks, we propose a high-resolution, compact convolutional network for
volumetric image segmentation. To illustrate its efficiency of learning 3D
representation from large-scale image data, the proposed network is validated
with the challenging task of parcellating 155 neuroanatomical structures from
brain MR images. Our experiments show that the proposed network architecture
compares favourably with state-of-the-art volumetric segmentation networks
while being an order of magnitude more compact. We consider the brain
parcellation task as a pretext task for volumetric image segmentation; our
trained network potentially provides a good starting point for transfer
learning. Additionally, we show the feasibility of voxel-level uncertainty
estimation using a sampling approximation through dropout.
