PyReconstruct: A fully open- source, collaborative successor to Reconstruct

• 成果类型: Article
• 署名作者: Chirillo, Michael A.; Falco, Julian N.; Musslewhite, Michael D.; Lindsey, Larry F.; Harris, Kristen M.
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8448
• DOI: 10.1073/pnas.2505822122
• 发表日期: 2025-08-05
• 关键词: scanning-electron-microscopy; volume; ultrastructure; brain; annotation; morphology; neurons; images; cells

摘要

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 found during annotation, 3) simplicity for nonspecialists within and beyond the neuroscience community, 4) a system to store experimental metadata with annotation data in a way that researchers remain masked regarding condition to avoid potential biases, 5) local management of large datasets appropriate for circuit-level analyses, and 6) 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.