  Advanced deep learning methods have been developed to conduct prostate MR
volume segmentation in either a 2D or 3D fully convolutional manner. However,
2D methods tend to have limited segmentation performance, since large amounts
of spatial information of prostate volumes are discarded during the
slice-by-slice segmentation process; and 3D methods also have room for
improvement, since they use isotropic kernels to perform 3D convolutions
whereas most prostate MR volumes have anisotropic spatial resolution. Besides,
the fully convolutional structural methods achieve good performance for
localization issues but neglect the per-voxel classification for segmentation
tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network
(3D GCA-Net) to address efficient prostate MR volume segmentation. We first
design a 3D ResNet encoder to extract 3D features from prostate scans, and then
develop the decoder, which is composed of a multi-scale 3D global convolutional
block and a 3D boundary refinement block, to address the classification and
localization issues simultaneously for volumetric segmentation. Additionally,
we combine the encoder-decoder segmentation network with an adversarial network
in the training phrase to enforce the contiguity of long-range spatial
predictions. Throughout the proposed model, we use anisotropic convolutional
processing for better feature learning on prostate MR scans. We evaluated our
3D GCA-Net model on two public prostate MR datasets and achieved
state-of-the-art performances.
