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...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
April 9, 2013
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| In: |
PLOS ONE
Year: 2013, Jahrgang: 8, Heft: 4, Pages: 1-10 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0061441 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pone.0061441 Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061441 
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| Verfasserangaben: | Bernd Lahrmann, Nektarios A. Valous, Urs Eisenmann, Nicolas Wentzensen, Niels Grabe |
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| 245 | 1 | 0 | |a Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides |c Bernd Lahrmann, Nektarios A. Valous, Urs Eisenmann, Nicolas Wentzensen, Niels Grabe |
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| 520 | |a 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. | ||
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Biomarkers | |
| 650 | 4 | |a Cell staining | |
| 650 | 4 | |a Cytology | |
| 650 | 4 | |a Image processing | |
| 650 | 4 | |a Imaging techniques | |
| 650 | 4 | |a Laboratory glassware | |
| 650 | 4 | |a Support vector machines | |
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