  This work presents and analyzes three convolutional neural network (CNN)
models for efficient pixelwise classification of images. When using
convolutional neural networks to classify single pixels in patches of a whole
image, a lot of redundant computations are carried out when using sliding
window networks. This set of new architectures solve this issue by either
removing redundant computations or using fully convolutional architectures that
inherently predict many pixels at once.
  The implementations of the three models are accessible through a new utility
on top of the Caffe library. The utility provides support for a wide range of
image input and output formats, pre-processing parameters and methods to
equalize the label histogram during training. The Caffe library has been
extended by new layers and a new backend for availability on a wider range of
hardware such as CPUs and GPUs through OpenCL.
  On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and
$320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding
window) network as the baseline. The label throughput is up to one megapixel
per second.
  The analyzed neural networks have distinctive characteristics that apply
during training or processing, and not every data set is suitable to every
architecture. The quality of the predictions is assessed on two neural tissue
data sets, of which one is the ISBI 2012 challenge data set. Two different loss
functions, Malis loss and Softmax loss, were used during training.
  The whole pipeline, consisting of models, interface and modified Caffe
library, is available as Open Source software under the working title Project
Greentea.
