Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

<p>Abstract</p> <p>Background</p> <p>High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The imag...

Full description

Bibliographic Details
Main Authors: Li Yizheng, LaPan Peter, Hill Andrew A, Haney Steve
Format: Article
Language:English
Published: BMC 2007-09-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/8/340
id doaj-2476c112afed4ab1ada7f7add7d579fd
record_format Article
spelling doaj-2476c112afed4ab1ada7f7add7d579fd2020-11-25T01:54:33ZengBMCBMC Bioinformatics1471-21052007-09-018134010.1186/1471-2105-8-340Impact of image segmentation on high-content screening data quality for SK-BR-3 cellsLi YizhengLaPan PeterHill Andrew AHaney Steve<p>Abstract</p> <p>Background</p> <p>High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.</p> <p>Results</p> <p>Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells.</p> <p>Conclusion</p> <p>Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.</p> http://www.biomedcentral.com/1471-2105/8/340
collection DOAJ
language English
format Article
sources DOAJ
author Li Yizheng
LaPan Peter
Hill Andrew A
Haney Steve
spellingShingle Li Yizheng
LaPan Peter
Hill Andrew A
Haney Steve
Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
BMC Bioinformatics
author_facet Li Yizheng
LaPan Peter
Hill Andrew A
Haney Steve
author_sort Li Yizheng
title Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
title_short Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
title_full Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
title_fullStr Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
title_full_unstemmed Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
title_sort impact of image segmentation on high-content screening data quality for sk-br-3 cells
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2007-09-01
description <p>Abstract</p> <p>Background</p> <p>High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.</p> <p>Results</p> <p>Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells.</p> <p>Conclusion</p> <p>Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.</p>
url http://www.biomedcentral.com/1471-2105/8/340
work_keys_str_mv AT liyizheng impactofimagesegmentationonhighcontentscreeningdataqualityforskbr3cells
AT lapanpeter impactofimagesegmentationonhighcontentscreeningdataqualityforskbr3cells
AT hillandrewa impactofimagesegmentationonhighcontentscreeningdataqualityforskbr3cells
AT haneysteve impactofimagesegmentationonhighcontentscreeningdataqualityforskbr3cells
_version_ 1724986674059935744