Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-lay...

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Autores principales: Bernd Lahrmann, Nektarios A Valous, Urs Eisenmann, Nicolas Wentzensen, Niels Grabe
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2013
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Acceso en línea:https://doaj.org/article/c4cdf36abfa54b27a2f4d2c8dca2c87d
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spelling oai:doaj.org-article:c4cdf36abfa54b27a2f4d2c8dca2c87d2021-11-18T07:49:59ZSemantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.1932-620310.1371/journal.pone.0061441https://doaj.org/article/c4cdf36abfa54b27a2f4d2c8dca2c87d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23585899/?tool=EBIhttps://doaj.org/toc/1932-6203Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.Bernd LahrmannNektarios A ValousUrs EisenmannNicolas WentzensenNiels GrabePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e61441 (2013)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Bernd Lahrmann
Nektarios A Valous
Urs Eisenmann
Nicolas Wentzensen
Niels Grabe
Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
description Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.
format article
author Bernd Lahrmann
Nektarios A Valous
Urs Eisenmann
Nicolas Wentzensen
Niels Grabe
author_facet Bernd Lahrmann
Nektarios A Valous
Urs Eisenmann
Nicolas Wentzensen
Niels Grabe
author_sort Bernd Lahrmann
title Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
title_short Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
title_full Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
title_fullStr Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
title_full_unstemmed Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
title_sort semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/c4cdf36abfa54b27a2f4d2c8dca2c87d
work_keys_str_mv AT berndlahrmann semanticfocusingallowsfullyautomatedsinglelayerslidescanningofcervicalcytologyslides
AT nektariosavalous semanticfocusingallowsfullyautomatedsinglelayerslidescanningofcervicalcytologyslides
AT urseisenmann semanticfocusingallowsfullyautomatedsinglelayerslidescanningofcervicalcytologyslides
AT nicolaswentzensen semanticfocusingallowsfullyautomatedsinglelayerslidescanningofcervicalcytologyslides
AT nielsgrabe semanticfocusingallowsfullyautomatedsinglelayerslidescanningofcervicalcytologyslides
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