An automated images-to-graphs framework for high resolution connectomics

Frontiers in Neuroinformatics
William R Gray RoncalGregory D Hager

Abstract

Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification). In this manuscript we present the first fully-automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction). To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version o...Continue Reading

References

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Citations

Apr 15, 2020·Annual Review of Neuroscience·Adam S CharlesJoshua T Vogelstein
Nov 21, 2018·Frontiers in Neuroinformatics·Elizabeth P ReillyMatthew J Roos
Jan 27, 2017··Wiktor JakubiukGergely Odor

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Methods Mentioned

BETA
electron microscopy
scanning electron microscopy

Software Mentioned

OCP
Open Connectome
MATLAB Application Programming Interface ( API )
Rhoana
MROCP
NoSQL
Gala
RAMON
GraphML
Apache

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