NEURD offers automated proofreading and feature extraction for connectomics

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, JoAnn Buchanan, Daniel J. Bumbarger, Manuel A. Castro, Erick Cobos, Sven Dorkenwald, Leila Elabbady, Akhilesh Halageri, Zhen Jia, Chris Jordan, Dan Kapner, Nico Kemnitz, Sam Kinn, Kisuk Lee, Kai Li, Ran Lu, Thomas Macrina, Gayathri Mahalingam, Eric Mitchell, Shanka Subhra Mondal, Shang Mu, Barak Nehoran, Sergiy Popovych, Casey M. Schneider-Mizell, William Silversmith, Marc Takeno, Russel Torres, Nicholas L. Turner, William Wong, Jingpeng Wu, Szi-chieh Yu, Wenjing Yin, Daniel Xenes, Lindsey M. Kitchell, Patricia K. Rivlin, Victoria A. Rose, Caitlyn A. Bishop, Brock Wester, Emmanouil Froudarakis, Edgar Y. Walker, Fabian Sinz, H. Sebastian Seung, Forrest Collman, Nuno Maçarico Costa, R. Clay Reid, Xaq Pitkow, Andreas S. Tolias and Jacob Reimer ()
Additional contact information
Brendan Celii: Baylor College of Medicine
Stelios Papadopoulos: Baylor College of Medicine
Zhuokun Ding: Baylor College of Medicine
Paul G. Fahey: Baylor College of Medicine
Eric Wang: Baylor College of Medicine
Christos Papadopoulos: Baylor College of Medicine
Alexander B. Kunin: Baylor College of Medicine
Saumil Patel: Baylor College of Medicine
J. Alexander Bae: 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
Sven Dorkenwald: Princeton University
Leila Elabbady: Allen Institute for Brain Science
Akhilesh Halageri: Princeton University
Zhen Jia: 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
Kai Li: Princeton University
Ran Lu: Princeton University
Thomas Macrina: Princeton University
Gayathri Mahalingam: Allen Institute for Brain Science
Eric Mitchell: Princeton University
Shanka Subhra Mondal: Princeton University
Shang Mu: Princeton University
Barak Nehoran: Princeton University
Sergiy Popovych: 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
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
Edgar Y. Walker: University of Washington
Fabian Sinz: Baylor College of Medicine
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
Andreas S. Tolias: Baylor College of Medicine
Jacob Reimer: Baylor College of Medicine

Nature, 2025, vol. 640, issue 8058, 487-496

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41586-025-08660-5

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