  Fully convolutional networks (FCNs), including UNet and VNet, are widely-used
network architectures for semantic segmentation in recent studies. However,
conventional FCN is typically trained by the cross-entropy or Dice loss, which
only calculates the error between predictions and ground-truth labels for
pixels individually. This often results in non-smooth neighborhoods in the
predicted segmentation. To address this problem, we propose a two-stage
framework, with the first stage to quickly localize the prostate region and the
second stage to precisely segment the prostate by a multi-task UNet
architecture. We introduce a novel online metric learning module through
voxel-wise sampling in the multi-task network. Therefore, the proposed network
has a dual-branch architecture that tackles two tasks: 1) a segmentation
sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric
learning sub-network aiming to improve the quality of the learned feature space
supervised by a metric loss. Specifically, the voxel-metric learning
sub-network samples tuples (including triplets and pairs) in voxel-level
through the intermediate feature maps. Unlike conventional deep metric learning
methods that generate triplets or pairs in image-level before the training
phase, our proposed voxel-wise tuples are sampled in an online manner and
operated in an end-to-end fashion via multi-task learning. To evaluate the
proposed method, we implement extensive experiments on a real CT image dataset
consisting of 339 patients. The ablation studies show that our method can
effectively learn more representative voxel-level features compared with the
conventional learning methods with cross-entropy or Dice loss. And the
comparisons show that the proposed method outperforms the state-of-the-art
methods by a reasonable margin.
