CAVE: Connectome Annotation Versioning Engine.

TitleCAVE: Connectome Annotation Versioning Engine.
Publication TypeJournal Article
Year of Publication2023
AuthorsDorkenwald S, Schneider-Mizell CM, Brittain D, Halageri A, Jordan C, Kemnitz N, Castro MA, Silversmith W, Maitin-Shephard J, Troidl J, Pfister H, Gillet V, Xenes D, J Bae A, Bodor AL, Buchanan JA, Bumbarger DJ, Elabbady L, Jia Z, Kapner D, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SSubhra, Mu S, Nehoran B, Popovych S, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu S-C, R Reid C, da Costa NMa├žarico, H Seung S, Collman F
JournalbioRxiv
Date Published2023 Jul 28
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 new 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 constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

DOI10.1101/2023.07.26.550598
Alternate JournalbioRxiv
PubMed ID37546753
PubMed Central IDPMC10402030
Grant ListRF1 MH123400 / MH / NIMH NIH HHS / United States
RF1 MH125932 / MH / NIMH NIH HHS / United States
RF1 MH129268 / MH / NIMH NIH HHS / United States
U24 NS126935 / NS / NINDS NIH HHS / United States