  Convolutional networks have become state-of-the-art techniques for automatic
medical image analysis, with the U-net architecture being the most popular at
this moment. In this article we report the application of a 3D version of U-net
to the automatic segmentation of prostate peripheral and transition zones in 3D
MRI images. Our results are slightly better than recent studies that used 2D
U-net and handcrafted feature approaches.
  In addition, we test ideas for improving the 3D U-net setup, by 1) letting
the network segment surrounding tissues, making use of the fixed anatomy, and
2) adjusting the network architecture to reflect the anisotropy in the
dimensions of the MRI image volumes. While the latter adjustment gave a
marginal improvement, the former adjustment showed a significant deterioration
of the network performance. We were able to explain this deterioration by
inspecting feature map activations in all layers of the network. We show that
to segment more tissues the network replaces feature maps that were dedicated
to detecting prostate peripheral zones, by feature maps detecting the
surrounding tissues.
