title: An automated images-to-graphs framework for high resolution connectomics.
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 in support of eventually unlocking new biofidelic computational primitives and understanding of neuropathologies.
authors: Department of Computer Science, Johns Hopkins University Baltimore, MD, USA ; Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.
authors: William R
authors: WR
authors: Gray Roncal
authors: Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.
authors: Dean M
authors: DM
authors: Kleissas
authors: Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA ; Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors: Joshua T
authors: JT
authors: Vogelstein
authors: Department of Computer Science, Johns Hopkins University Baltimore, MD, USA.
authors: Priya
authors: P
authors: Manavalan
authors: Department of Computer Science, Johns Hopkins University Baltimore, MD, USA.
authors: Kunal
authors: K
authors: Lillaney
authors: Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.
authors: Michael
authors: M
authors: Pekala
authors: Department of Computer Science, Johns Hopkins University Baltimore, MD, USA.
authors: Randal
authors: R
authors: Burns
authors: Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.
authors: R Jacob
authors: RJ
authors: Vogelstein
authors: Department of Applied Math and Statistics, Johns Hopkins University Baltimore, MD, USA.
authors: Carey E
authors: CE
authors: Priebe
authors: Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkins University Laurel, MD, USA.
authors: Mark A
authors: MA
authors: Chevillet
authors: Department of Computer Science, Johns Hopkins University Baltimore, MD, USA.
authors: Gregory D
authors: GD
authors: Hager
keywords: big data; computer vision; connectomics; electron microscopy; framework; graph error; images to graphs; pipeline