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CAVE: Connectome Annotation Versioning Engine.

Sven Dorkenwald1,2, Casey M Schneider-Mizell3, Derrick Brittain3

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

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|April 9, 2025
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Summary
This summary is machine-generated.

The Connectome Annotation Versioning Engine (CAVE) enables collaborative proofreading and analysis of large-scale brain connectomic datasets. This infrastructure supports reproducible research by managing data changes during ongoing annotation and segmentation correction.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Bioinformatics

Background:

  • Large-scale connectomic datasets are growing due to advances in electron microscopy and image segmentation.
  • Collaborative annotation and error correction (proofreading) are essential for these datasets.
  • Managing dynamic data changes in large connectomes presents significant analytical challenges.

Purpose of the Study:

  • To present the Connectome Annotation Versioning Engine (CAVE) as a computational infrastructure.
  • To provide scalable solutions for proofreading and flexible annotation support.
  • To enable reproducible connectome analysis on petascale datasets during ongoing annotation.

Main Methods:

  • Developed CAVE as a suite of web services.
  • Implemented scalable solutions for concurrent annotation and proofreading.
  • Enabled fast analysis queries at arbitrary time points.

Main Results:

  • CAVE supports distributed communities in analyzing large-scale connectomic data.
  • The system handles millions of voxel relabelings and thousands of annotation edits.
  • Reproducible analysis is achievable even with ongoing data modification.

Conclusions:

  • CAVE provides essential infrastructure for collaborative and reproducible connectome research.
  • It addresses the challenges of managing dynamic, large-scale neuroscientific datasets.
  • Facilitates efficient analysis of petascale connectomic data.