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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.
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PyReconstruct: A fully opensource, collaborative successor to Reconstruct
Abstract As the serial section community transitions to volume electron microscopy, tools are needed to balance rapid segmentation efforts with documenting the fine detail of structures that support cell function. New annotation applications should be accessible to users and meet the needs of the neuroscience and connectomics communities while also being useful across other disciplines. Issues not currently addressed by a single, modern annotation application include: 1) built-in curation systems with utilities for expert intervention to provide quality assurance, 2) integrated alignment features that allow for image registration on-the-fly as image flaws are discovered during annotation, 3) simplicity for non-specialists within and beyond the neuroscience community, 5) a system to store experimental meta-data with annotation data in a way that researchers remain masked regarding condition to avoid potential biases, 6) local management of large datasets, 7) fully open-source codebase allowing development of new tools, and more. Here, we present PyReconstruct, a modern successor to the Reconstruct annotation tool. PyReconstruct operates in a field-agnostic manner, runs on all major operating systems, breaks through legacy RAM limitations, features an intuitive and collaborative curation system, and employs a flexible and dynamic approach to image registration. It can be used to analyze, display, and publish experimental or connectomics data. PyReconstruct is suited for generating ground truth to implement in automated segmentation, outcomes of which can be returned to PyReconstruct for proofreading and quality control. Significance statementIn neuroscience, the emerging field of connectomics has produced annotation tools for reconstruction that prioritize circuit connectivity across microns to centimeters and farther. Determining the strength of synapses forming the connections is crucial to understand function and requires quantification of their nanoscale dimensions and subcellular composition. PyReconstruct, successor to the early annotation tool Reconstruct, meets these requirements for synapses and other structures well beyond neuroscience. PyReconstruct lifts many restrictions of legacy Reconstruct and offers a user-friendly interface, integrated curation, dynamic alignment, nanoscale quantification, 3D visualization, and more. Extensive compatibility with third-party software provides access to the expanding tools from the connectomics and imaging communities.
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- Award ID(s):
- 2014862
- PAR ID:
- 10623763
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.1101/2025.04.21.649793
