Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach
Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approache...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 July 2012
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| In: |
Scientific reports
Year: 2012, Jahrgang: 2 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/srep00503 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1038/srep00503 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/srep00503 
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| Verfasserangaben: | Stephan Wienert, Daniel Heim, Kai Saeger, Albrecht Stenzinger, Michael Beil, Peter Hufnagl, Manfred Dietel, Carsten Denkert & Frederick Klauschen |
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| 520 | |a Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based “minimum-model” cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training. | ||
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