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

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 a...

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Vydáno v:Frontiers in neural circuits Ročník 16; s. 977700
Hlavní autoři: Silversmith, William, Zlateski, Aleksandar, Bae, J. Alexander, Tartavull, Ignacio, Kemnitz, Nico, Wu, Jingpeng, Seung, H. Sebastian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Research Foundation 25.11.2022
Frontiers Media S.A
Témata:
Algorithms
Annotations
Cloud computing
connectomics
Consortia
Data storage
Datasets
Editing
Electron microscopy
Image processing
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval
meshing
Microscopy
Microscopy, Electron
Neurons
Neuroscience
Open source software
Segmentation
skeletonization
Visualization
image processing
connectomics
meshing
neuroscience
skeletonization
cloud computing
compression
distributed computing
ISSN:1662-5110, 1662-5110
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Shrnutí: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.
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Reviewed by: Mohan Raghavan, Indian Institute of Technology Hyderabad, India; Kevin Boergens, Paradromics, Inc., United States
Edited by: Yoshiyuki Kubota, National Institute for Physiological Sciences (NIPS), Japan
ISSN:1662-5110
1662-5110
DOI:10.3389/fncir.2022.977700