CAVE: Connectome Annotation Versioning Engine

Advances in electron microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets, which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this changing and expanding data landscape. Here we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure that provides scalable solutions for proofreading and flexible annotation support for fast analysis queries at arbitrary time points. Deployed as a suite of web services, CAVE empowers distributed communities to perform reproducible connectome analysis in up to petascale datasets (~1 mm<sup>3</sup>) while proofreading and annotating is ongoing.

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Publication Details

Journal
Nature Methods
Published
2025-04-09
DOI
https://doi.org/10.1038/s41592-024-02426-z
Citations
32
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
50.65

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article

CAVE: Connectome Annotation Versioning Engine

Ágnes L. Bodor, R. Clay Reid, Sergiy Popovych, JoAnn Buchanan et al.
32 citations
Nature Methods
Cell Image Analysis Techniques
50.65
article

CAVE: Connectome Annotation Versioning Engine

Ágnes L. Bodor, R. Clay Reid, Sergiy Popovych, JoAnn Buchanan, Szi-chieh Yu, Nuno Maçarico da Costa, Thomas Macrina, Zhen Jia, Russel Torres, Sven Dorkenwald, Shanka Subhra Mondal, Leila Elabbady, Ran Lu, Nicholas L. Turner, Hanspeter Pfister, H. Sebastian Seung, William Silversmith, Nico Kemnitz, Jingpeng Wu, Sam Kinn, Marc Takeno, Daniel J. Bumbarger, Shang Mu, Akhilesh Halageri, Daniel Xenes, Casey M Schneider-Mizell, Eric Mitchell, Gayathri Mahalingam, Jakob Troidl, Forrest Collman, Barak Nehoran, Valentin Gillet, Derrick Brittain, Daniel Kapner, Jeremy Maitin-Shephard, Kai Li, J. Alexander Bae, Kisuk Lee, William Wong, Manual A. Castro, Wenjing Yin, Chris Jordan
article en
32 citations

Abstract

Advances in electron microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets, which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this changing and expanding data landscape. Here we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure that provides scalable solutions for proofreading and flexible annotation support for fast analysis queries at arbitrary time points. Deployed as a suite of web services, CAVE empowers distributed communities to perform reproducible connectome analysis in up to petascale datasets (~1 mm<sup>3</sup>) while proofreading and annotating is ongoing.

Nature MethodsVol. 22(5)
Google (United States) (US), Allen Institute for Brain Science (US), Harvard University (US), Lund University (SE), Princeton University (US), Johns Hopkins University Applied Physics Laboratory (US), Allen Institute (US), Massachusetts Institute of Technology (US)
National Science Foundation, University of Washington, Lunds Universitet, National Institutes of Health, Intelligence Advanced Research Projects Activity, Interior Business Center, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, Division of Information and Intelligent Systems
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Cell Image Analysis Techniques
50.65
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