Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling

Author:

Silversmith William,Zlateski Aleksandar,Bae J. Alexander,Tartavull Ignacio,Kemnitz Nico,Wu Jingpeng,Seung H. Sebastian

Abstract

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Funder

Intelligence Advanced Research Projects Activity

National Institute of Mental Health

National Institute of Neurological Disorders and Stroke

National Eye Institute

Army Research Office

Publisher

Frontiers Media SA

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience,Sensory Systems,Neuroscience (miscellaneous)

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