  Understanding the connectivity in the brain is an important prerequisite for
understanding how the brain processes information. In the Brain/MINDS project,
a connectivity study on marmoset brains uses two-photon microscopy fluorescence
images of axonal projections to collect the neuron connectivity from defined
brain regions at the mesoscopic scale. The processing of the images requires
the detection and segmentation of the axonal tracer signal. The objective is to
detect as much tracer signal as possible while not misclassifying other
background structures as the signal. This can be challenging because of imaging
noise, a cluttered image background, distortions or varying image contrast
cause problems.
  We are developing MarmoNet, a pipeline that processes and analyzes tracer
image data of the common marmoset brain. The pipeline incorporates
state-of-the-art machine learning techniques based on artificial convolutional
neural networks (CNN) and image registration techniques to extract and map all
relevant information in a robust manner. The pipeline processes new images in a
fully automated way.
  This report introduces the current state of the tracer signal analysis part
of the pipeline.
