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Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting
Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019880/ https://www.ncbi.nlm.nih.gov/pubmed/24847213 http://dx.doi.org/10.3389/fnana.2014.00027 |
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author | Schmitz, Christoph Eastwood, Brian S. Tappan, Susan J. Glaser, Jack R. Peterson, Daniel A. Hof, Patrick R. |
author_facet | Schmitz, Christoph Eastwood, Brian S. Tappan, Susan J. Glaser, Jack R. Peterson, Daniel A. Hof, Patrick R. |
author_sort | Schmitz, Christoph |
collection | PubMed |
description | Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. |
format | Online Article Text |
id | pubmed-4019880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-40198802014-05-20 Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting Schmitz, Christoph Eastwood, Brian S. Tappan, Susan J. Glaser, Jack R. Peterson, Daniel A. Hof, Patrick R. Front Neuroanat Neuroscience Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. Frontiers Media S.A. 2014-05-07 /pmc/articles/PMC4019880/ /pubmed/24847213 http://dx.doi.org/10.3389/fnana.2014.00027 Text en Copyright © 2014 Schmitz, Eastwood, Tappan, Glaser, Peterson and Hof. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Schmitz, Christoph Eastwood, Brian S. Tappan, Susan J. Glaser, Jack R. Peterson, Daniel A. Hof, Patrick R. Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title | Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title_full | Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title_fullStr | Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title_full_unstemmed | Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title_short | Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting |
title_sort | current automated 3d cell detection methods are not a suitable replacement for manual stereologic cell counting |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019880/ https://www.ncbi.nlm.nih.gov/pubmed/24847213 http://dx.doi.org/10.3389/fnana.2014.00027 |
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