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Understanding the connectivity in the brain is an important prerequisite forunderstanding how the brain processes information. In the Brain/MINDS project,a connectivity study on marmoset brains uses two-photon microscopy fluorescenceimages of axonal projections to collect the neuron connectivity from definedbrain regions at the mesoscopic scale. The processing of the images requiresthe detection and segmentation of the axonal tracer signal. The objective is todetect as much tracer signal as possible while not misclassifying otherbackground structures as the signal. This can be challenging because of imagingnoise, a cluttered image background, distortions or varying image contrastcause problems.  We are developing MarmoNet, a pipeline that processes and analyzes tracerimage data of the common marmoset brain. The pipeline incorporatesstate-of-the-art machine learning techniques based on artificial convolutionalneural networks (CNN) and image registration techniques to extract and map allrelevant information in a robust manner. The pipeline processes new images in afully automated way.  This report introduces the current state of the tracer signal analysis partof the pipeline.

MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography