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

Authors :
William Silversmith
Aleksandar Zlateski
J. Alexander Bae
Ignacio Tartavull
Nico Kemnitz
Jingpeng Wu
H. Sebastian Seung
Source :
Frontiers in Neural Circuits, Vol 16 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

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.

Details

Language :
English
ISSN :
16625110
Volume :
16
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neural Circuits
Publication Type :
Academic Journal
Accession number :
edsdoj.67106399db1f4c819fa84e17870aa527
Document Type :
article
Full Text :
https://doi.org/10.3389/fncir.2022.977700