Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: 
Convolutional networks for biomedical image segmentation. In 
International Conference on Medical image computing and computer-assisted 
intervention (pp. 234-241). Cham: Springer international publishing.

Abstract
There is large consent that successful training of deep networks 
requires many thousand annotated training samples. In this paper, 
we present a network and training strategy that relies on the strong
use of data augmentation to use the available annotated samples more
efficiently. The architecture consists of a contracting path to capture
context and a symmetric expanding path that enables precise localization. 
We show that such a network can be trained end-to-end from very
few images and outperforms the prior best method (a sliding-window
convolutional network) on the ISBI challenge for segmentation of neuronal
structures in electron microscopic stacks. Using the same net-
work trained on transmitted light microscopy images (phase contrast
and DIC) we won the ISBI cell tracking challenge 2015 in these categories 
by a large margin. Moreover, the network is fast. Segmentation of a 512x512 
image takes less than a second on a recent GPU. The full implementation 
(based on Cae) and the trained networks are available
at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.