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Verfasst von:Lahrmann, Bernd [VerfasserIn]   i
 Valous, Nektarios A. [VerfasserIn]   i
 Eisenmann, Urs [VerfasserIn]   i
 Wentzensen, Nicolas [VerfasserIn]   i
 Grabe, Niels [VerfasserIn]   i
Titel:Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides
Verf.angabe:Bernd Lahrmann, Nektarios A. Valous, Urs Eisenmann, Nicolas Wentzensen, Niels Grabe
E-Jahr:2013
Jahr:April 9, 2013
Umfang:10 S.
Fussnoten:Gesehen am 22.09.2021
Titel Quelle:Enthalten in: PLOS ONE
Ort Quelle:San Francisco, California, US : PLOS, 2006
Jahr Quelle:2013
Band/Heft Quelle:8(2013), 4 vom: Apr., Artikel-ID e61441, Seite 1-10
ISSN Quelle:1932-6203
Abstract: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.
DOI:doi:10.1371/journal.pone.0061441
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1371/journal.pone.0061441
 Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061441
 DOI: https://doi.org/10.1371/journal.pone.0061441
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Algorithms
 Biomarkers
 Cell staining
 Cytology
 Image processing
 Imaging techniques
 Laboratory glassware
 Support vector machines
K10plus-PPN:177160462X
Verknüpfungen:→ Zeitschrift

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