  Multi-parametric MR images have been shown to be effective in the
non-invasive diagnosis of prostate cancer. Automated segmentation of the
prostate eliminates the need for manual annotation by a radiologist which is
time consuming. This improves efficiency in the extraction of imaging features
for the characterization of prostate tissues. In this work, we propose a fully
automated cascaded deep learning architecture with residual blocks, Cascaded
MRes-UNET, for segmentation of the prostate gland and the peripheral zone in
one pass through the network. The network yields high Dice scores
($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in
prostate segmentation compared to manual annotations by an experienced
radiologist. The average difference in total prostate volume estimation is less
than 5%.
