  In the brain, the structure of a network of neurons defines how these neurons
implement the computations that underlie the mind and the behavior of animals
and humans. Provided that we can describe the network of neurons as a graph, we
can employ methods from graph theory to investigate its structure or use
cellular automata to mathematically assess its function. Although, software for
the analysis of graphs and cellular automata are widely available. Graph
extraction from the image of networks of brain cells remains difficult. Nervous
tissue is heterogeneous, and differences in anatomy may reflect relevant
differences in function. Here we introduce a deep learning based toolbox to
extracts graphs from images of brain tissue. This toolbox provides an
easy-to-use framework allowing system neuroscientists to generate graphs based
on images of brain tissue by combining methods from image processing, deep
learning, and graph theory. The goals are to simplify the training and usage of
deep learning methods for computer vision and facilitate its integration into
graph extraction pipelines. In this way, the toolbox provides an alternative to
the required laborious manual process of tracing, sorting and classifying. We
expect to democratize the machine learning methods to a wider community of
users beyond the computer vision experts and improve the time-efficiency of
graph extraction from large brain image datasets, which may lead to further
understanding of the human mind.
