An Automated Images-to-Graphs Framework for High Resolution Connectomics

TL;DR

This paper introduces the first fully automated pipeline converting high-resolution EM images into neuronal connectivity graphs, enabling large-scale brain network analysis without human intervention.

Contribution

It presents a novel end-to-end automated images-to-graphs framework for connectomics, including a new metric for graph quality assessment and deployment on large datasets.

Findings

Achieved fully automated reconstruction of neural connectivity graphs

Developed a new metric for evaluating graph quality

Provided baseline results on a large public dataset

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 of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available toward eventually unlocking new biofidelic computational primitives and understanding of neuropathologies.