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Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach

Wienert, Stephan ; Heim, Daniel ; Saeger, Kai ; Stenzinger, Albrecht ; Beil, Michael ; Hufnagl, Peter ; Dietel, Manfred ; Denkert, Carsten ; Klauschen, Frederick

Scientific reports, 2012-07, Vol.2 (1), p.503-503, Article 503 [Periódico revisado por pares]

England: Nature Publishing Group

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Título:
Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach
Autor: Wienert, Stephan ; Heim, Daniel ; Saeger, Kai ; Stenzinger, Albrecht ; Beil, Michael ; Hufnagl, Peter ; Dietel, Manfred ; Denkert, Carsten ; Klauschen, Frederick
Assuntos: Algorithms ; Cell Nucleus - ultrastructure ; Cell size ; Computational Biology - methods ; Humans ; Image processing ; Image Processing, Computer-Assisted - methods ; Informatics ; Microscopy ; Microscopy - methods ; Nuclei ; Segmentation ; Software
É parte de: Scientific reports, 2012-07, Vol.2 (1), p.503-503, Article 503
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

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Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach

Wienert, Stephan ; Heim, Daniel ; Saeger, Kai ; Stenzinger, Albrecht ; Beil, Michael ; Hufnagl, Peter ; Dietel, Manfred ; Denkert, Carsten ; Klauschen, Frederick

England: Nature Publishing Group

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