Open Access
Frontiers in Neuroanatomy, volume 11
DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D
Shuvaev Sergey A
1, 2
,
Lazutkin Alexander A
2, 3, 4
,
Kedrov Alexander V
2, 4
,
Anokhin Konstantin V
4, 5
,
Enikolopov Grigori N
2, 3
,
Koulakov Alexei A.
1
1
Cold Spring Harbor Laboratory, United States
|
Publication type: Journal Article
Publication date: 2017-12-12
Journal:
Frontiers in Neuroanatomy
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 2.9
ISSN: 16625129
Cellular and Molecular Neuroscience
Anatomy
Neuroscience (miscellaneous)
Abstract
Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.
Citations by journals
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1
2
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eLife
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eLife
2 publications, 13.33%
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Aging
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Aging
1 publication, 6.67%
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Scientific Reports
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Scientific Reports
1 publication, 6.67%
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MethodsX
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MethodsX
1 publication, 6.67%
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Neuroscience
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Neuroscience
1 publication, 6.67%
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SLAS Technology
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SLAS Technology
1 publication, 6.67%
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Journal of Comparative Neurology
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Journal of Comparative Neurology
1 publication, 6.67%
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Cell Reports
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Cell Reports
1 publication, 6.67%
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1
2
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Citations by publishers
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1
2
3
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Elsevier
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Elsevier
3 publications, 20%
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eLife Sciences Publications
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eLife Sciences Publications
2 publications, 13.33%
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Impact Journals
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Impact Journals
1 publication, 6.67%
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Springer Nature
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Springer Nature
1 publication, 6.67%
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SAGE
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SAGE
1 publication, 6.67%
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Wiley
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Wiley
1 publication, 6.67%
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IEEE
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IEEE
1 publication, 6.67%
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1
2
3
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Shuvaev S. A. et al. DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D // Frontiers in Neuroanatomy. 2017. Vol. 11.
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Shuvaev S. A., Lazutkin A. A., Kedrov A. V., Anokhin K. V., Enikolopov G. N., Koulakov A. A. DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D // Frontiers in Neuroanatomy. 2017. Vol. 11.
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TY - JOUR
DO - 10.3389/fnana.2017.00117
UR - https://doi.org/10.3389%2Ffnana.2017.00117
TI - DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D
T2 - Frontiers in Neuroanatomy
AU - Shuvaev, Sergey A
AU - Lazutkin, Alexander A
AU - Kedrov, Alexander V
AU - Anokhin, Konstantin V
AU - Enikolopov, Grigori N
AU - Koulakov, Alexei A.
PY - 2017
DA - 2017/12/12 00:00:00
PB - Frontiers Media S.A.
VL - 11
SN - 1662-5129
ER -
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@article{2017_Shuvaev,
author = {Sergey A Shuvaev and Alexander A Lazutkin and Alexander V Kedrov and Konstantin V Anokhin and Grigori N Enikolopov and Alexei A. Koulakov},
title = {DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D},
journal = {Frontiers in Neuroanatomy},
year = {2017},
volume = {11},
publisher = {Frontiers Media S.A.},
month = {dec},
url = {https://doi.org/10.3389%2Ffnana.2017.00117},
doi = {10.3389/fnana.2017.00117}
}
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