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CAVE: Connectome Annotation Versioning Engine

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Bibliographic Details
Title: CAVE: Connectome Annotation Versioning Engine
Authors: Dorkenwald, Sven, Schneider-Mizell, Casey M., Brittain, Derrick, Halageri, Akhilesh, Jordan, Chris, Kemnitz, Nico, Castro, Manual A., Silversmith, William, Maitin-Shephard, Jeremy, Troidl, Jakob, Pfister, Hanspeter, Gillet, Valentin, Xenes, Daniel, Bae, J. Alexander, Bodor, Agnes L., Buchanan, Jo Ann, Bumbarger, Daniel J., Elabbady, Leila, Jia, Zhen, Kapner, Daniel, Kinn, Sam, Lee, Kisuk, Li, Kai, Lu, Ran, Macrina, Thomas, Mahalingam, Gayathri, Mitchell, Eric, Mondal, Shanka Subhra, Mu, Shang, Nehoran, Barak, Popovych, Sergiy, Takeno, Marc, Torres, Russel, Turner, Nicholas L., Wong, William, Wu, Jingpeng, Yin, Wenjing, Yu, Szi Chieh, Reid, R. Clay, da Costa, Nuno Maçarico, Seung, H. Sebastian, Collman, Forrest
Contributors: Lund University, Faculty of Science, Department of Biology, Sections at the Department of Biology, Sensory Biology, Lunds universitet, Naturvetenskapliga fakulteten, Biologiska institutionen, Avdelningar vid Biologiska institutionen, Sinnesbiologi, Originator, Lund University, Faculty of Science, Department of Biology, Research groups at the Department of Biology, Lund Vision Group, Lunds universitet, Naturvetenskapliga fakulteten, Biologiska institutionen, Forskargrupper vid Biologiska institutionen, Syngruppen, Originator
Source: Nature Methods. 22(5):1112-1120
Subject Terms: Medical and Health Sciences, Basic Medicine, Neurosciences, Medicin och hälsovetenskap, Medicinska och farmaceutiska grundvetenskaper, Neurovetenskaper, Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Computer Systems, Teknik, Elektroteknik och elektronik, Datorsystem, Medical Engineering, Medical Imaging, Medicinteknik, Medicinsk bildvetenskap
Description: 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 mm3) while proofreading and annotating is ongoing.
Access URL: https://doi.org/10.1038/s41592-024-02426-z
Database: SwePub
Description
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 mm3) while proofreading and annotating is ongoing.
ISSN:15487091
15487105
DOI:10.1038/s41592-024-02426-z