  The high incidence rate of prostate disease poses a requirement in early
detection for diagnosis. As one of the main imaging methods used for prostate
cancer detection, Magnetic Resonance Imaging (MRI) has wide range of appearance
and imbalance problems, making automated prostate segmentation fundamental but
challenging. Here we propose a novel Densely Dilated Spatial Pooling
Convolutional Network (DDSP ConNet) in encoder-decoder structure. It employs
dense structure to combine dilated convolution and global pooling, thus
supplies coarse segmentation results from encoder and decoder subnet and
preserves more contextual information. To obtain richer hierarchical feature
maps, residual long connection is furtherly adopted to fuse contexture
features. Meanwhile, we adopt DSC loss and Jaccard loss functions to train our
DDSP ConNet. We surprisingly found and proved that, in contrast to re-weighted
cross entropy, DSC loss and Jaccard loss have a lot of benign properties in
theory, including symmetry, continuity and differentiability about the
parameters of network. Extensive experiments on the MICCAI PROMISE12 challenge
dataset have been done to corroborate the effectiveness of our DDSP ConNet with
DSC loss and Jaccard loss. Totally, our method achieves a score of 85.78 in the
test dataset, outperforming most of other competitors.
