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Nat Commun. 2019 Apr 4;10(1):1549. doi: 10.1038/s41467-019-09515-0.

Precise segmentation of densely interweaving neuron clusters using G-Cut.

Author information

1
Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications, Department of Cognitive Science, School of Informatics, Xiamen University, Xiamen, 361005, China.
2
Center for Integrative Connectomics, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90095, USA.
3
National Engineering Research Center for E-Learning, Central China Normal University, 430079, Wuhan, China.
4
Intuitive Surgical Inc., 1020 Kifer Road, Sunnyvale, CA, 94086, USA.
5
Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90095, USA.
6
Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
7
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
8
Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications, Department of Cognitive Science, School of Informatics, Xiamen University, Xiamen, 361005, China. jszhang@outlook.com.
9
National Engineering Research Center for E-Learning, Central China Normal University, 430079, Wuhan, China. jszhang@outlook.com.
10
Center for Integrative Connectomics, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90095, USA. hongwei.dong@loni.usc.edu.
11
Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, 90095, USA. hongwei.dong@loni.usc.edu.
12
Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90095, USA. hongwei.dong@loni.usc.edu.

Abstract

Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.

PMID:
30948706
PMCID:
PMC6449501
DOI:
10.1038/s41467-019-09515-0
[Indexed for MEDLINE]
Free PMC Article

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