VAST (Volume Annotation and Segmentation Tool): Efficient Manual and Semi-Automatic Labeling of Large 3D Image Stacks

Recent developments in serial-section electron microscopy allow the efficient generation of very large image data sets but analyzing such data poses challenges for software tools. Here we introduce Volume Annotation and Segmentation Tool (VAST), a freely available utility program for generating and...

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Bibliographic Details
Published in:Frontiers in neural circuits Vol. 12; p. 88
Main Authors: Berger, Daniel R., Seung, H. Sebastian, Lichtman, Jeff W.
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 16.10.2018
Frontiers Media S.A
Subjects:
Automation
Big Data
Bioinformatics
CLEM
connectomics
Data analysis
Datasets
Electron microscopy
Exploration
Image processing
Insects
Interactive learning
Nematodes
Nervous system
Neuroscience
Neurosciences
proofreading
Scanning electron microscopy
Segmentation
serial section electron microscopy
Software
visualization
United States > US
connectomics
serial section electron microscopy
visualization
voxel
proofreading
segmentation
CLEM
TrakEM2
ISSN:1662-5110, 1662-5110
Online Access:Get full text
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Summary:Recent developments in serial-section electron microscopy allow the efficient generation of very large image data sets but analyzing such data poses challenges for software tools. Here we introduce Volume Annotation and Segmentation Tool (VAST), a freely available utility program for generating and editing annotations and segmentations of large volumetric image (voxel) data sets. It provides a simple yet powerful user interface for real-time exploration and analysis of large data sets even in the Petabyte range.
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Reviewed by: Sean L. Hill, Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Canada; Marta Costa, University of Cambridge, United Kingdom; Sean Parsons, McMaster University, Canada
Edited by: Yoshiyuki Kubota, National Institute for Physiological Sciences (NIPS), Japan
ISSN:1662-5110
1662-5110
DOI:10.3389/fncir.2018.00088