DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural Networks

TL;DR

DeepTEGINN is a deep learning toolbox designed to automatically extract neural network graphs from brain tissue images, simplifying analysis and enabling broader use in neuroscience research.

Contribution

It introduces a user-friendly deep learning framework that automates graph extraction from brain images, reducing manual effort and integrating image processing with graph theory.

Findings

Automates graph extraction from brain tissue images.

Reduces manual labor in neural network analysis.

Facilitates large-scale brain image data analysis.

Abstract

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.