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NEURD offers automated proofreading and feature extraction for connectomics

Author

Listed:
  • Brendan Celii

    (Baylor College of Medicine
    Baylor College of Medicine
    Rice University
    Johns Hopkins University Applied Physics Laboratory)

  • Stelios Papadopoulos

    (Baylor College of Medicine
    Baylor College of Medicine
    Stanford University
    Stanford University)

  • Zhuokun Ding

    (Baylor College of Medicine
    Baylor College of Medicine
    Stanford University
    Stanford University)

  • Paul G. Fahey

    (Baylor College of Medicine
    Baylor College of Medicine
    Stanford University
    Stanford University)

  • Eric Wang

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Christos Papadopoulos

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Alexander B. Kunin

    (Baylor College of Medicine
    Baylor College of Medicine
    Creighton University)

  • Saumil Patel

    (Baylor College of Medicine
    Baylor College of Medicine
    Stanford University
    Stanford University)

  • J. Alexander Bae

    (Princeton University
    Princeton University)

  • Agnes L. Bodor

    (Allen Institute for Brain Science)

  • Derrick Brittain

    (Allen Institute for Brain Science)

  • JoAnn Buchanan

    (Allen Institute for Brain Science)

  • Daniel J. Bumbarger

    (Allen Institute for Brain Science)

  • Manuel A. Castro

    (Princeton University)

  • Erick Cobos

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Sven Dorkenwald

    (Princeton University
    Princeton University)

  • Leila Elabbady

    (Allen Institute for Brain Science)

  • Akhilesh Halageri

    (Princeton University)

  • Zhen Jia

    (Princeton University
    Princeton University)

  • Chris Jordan

    (Princeton University)

  • Dan Kapner

    (Allen Institute for Brain Science)

  • Nico Kemnitz

    (Princeton University)

  • Sam Kinn

    (Allen Institute for Brain Science)

  • Kisuk Lee

    (Princeton University
    Massachusetts Institute of Technology)

  • Kai Li

    (Princeton University)

  • Ran Lu

    (Princeton University)

  • Thomas Macrina

    (Princeton University
    Princeton University)

  • Gayathri Mahalingam

    (Allen Institute for Brain Science)

  • Eric Mitchell

    (Princeton University)

  • Shanka Subhra Mondal

    (Princeton University
    Princeton University)

  • Shang Mu

    (Princeton University)

  • Barak Nehoran

    (Princeton University
    Princeton University)

  • Sergiy Popovych

    (Princeton University
    Princeton University)

  • Casey M. Schneider-Mizell

    (Allen Institute for Brain Science)

  • William Silversmith

    (Princeton University)

  • Marc Takeno

    (Allen Institute for Brain Science)

  • Russel Torres

    (Allen Institute for Brain Science)

  • Nicholas L. Turner

    (Princeton University
    Princeton University)

  • William Wong

    (Princeton University)

  • Jingpeng Wu

    (Princeton University)

  • Szi-chieh Yu

    (Princeton University)

  • Wenjing Yin

    (Allen Institute for Brain Science)

  • Daniel Xenes

    (Johns Hopkins University Applied Physics Laboratory)

  • Lindsey M. Kitchell

    (Johns Hopkins University Applied Physics Laboratory)

  • Patricia K. Rivlin

    (Johns Hopkins University Applied Physics Laboratory)

  • Victoria A. Rose

    (Johns Hopkins University Applied Physics Laboratory)

  • Caitlyn A. Bishop

    (Johns Hopkins University Applied Physics Laboratory)

  • Brock Wester

    (Johns Hopkins University Applied Physics Laboratory)

  • Emmanouil Froudarakis

    (Baylor College of Medicine
    Baylor College of Medicine
    Foundation for Research and Technology Hellas)

  • Edgar Y. Walker

    (University of Washington
    University of Washington)

  • Fabian Sinz

    (Baylor College of Medicine
    Baylor College of Medicine
    University Tübingen
    University Göttingen)

  • H. Sebastian Seung

    (Princeton University)

  • Forrest Collman

    (Allen Institute for Brain Science)

  • Nuno Maçarico Costa

    (Allen Institute for Brain Science)

  • R. Clay Reid

    (Allen Institute for Brain Science)

  • Xaq Pitkow

    (Baylor College of Medicine
    Baylor College of Medicine
    Rice University
    Carnegie Mellon University)

  • Andreas S. Tolias

    (Baylor College of Medicine
    Baylor College of Medicine
    Rice University
    Stanford University)

  • Jacob Reimer

    (Baylor College of Medicine
    Baylor College of Medicine)

Abstract

We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning3–6. Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

Suggested Citation

  • Brendan Celii & Stelios Papadopoulos & Zhuokun Ding & Paul G. Fahey & Eric Wang & Christos Papadopoulos & Alexander B. Kunin & Saumil Patel & J. Alexander Bae & Agnes L. Bodor & Derrick Brittain & JoA, 2025. "NEURD offers automated proofreading and feature extraction for connectomics," Nature, Nature, vol. 640(8058), pages 487-496, April.
  • Handle: RePEc:nat:nature:v:640:y:2025:i:8058:d:10.1038_s41586-025-08660-5
    DOI: 10.1038/s41586-025-08660-5
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