Title |
Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach
|
---|---|
Published in |
Scientific Reports, July 2012
|
DOI | 10.1038/srep00503 |
Pubmed ID | |
Authors |
Stephan Wienert, Daniel Heim, Kai Saeger, Albrecht Stenzinger, Michael Beil, Peter Hufnagl, Manfred Dietel, Carsten Denkert, Frederick Klauschen |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Spain | 1 | 33% |
United Kingdom | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 3 | 1% |
United Kingdom | 2 | <1% |
Mexico | 2 | <1% |
United States | 2 | <1% |
Brazil | 1 | <1% |
Czechia | 1 | <1% |
Canada | 1 | <1% |
Italy | 1 | <1% |
Denmark | 1 | <1% |
Other | 1 | <1% |
Unknown | 213 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 66 | 29% |
Researcher | 38 | 17% |
Student > Master | 30 | 13% |
Student > Bachelor | 18 | 8% |
Student > Doctoral Student | 11 | 5% |
Other | 29 | 13% |
Unknown | 36 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 61 | 27% |
Engineering | 47 | 21% |
Agricultural and Biological Sciences | 28 | 12% |
Medicine and Dentistry | 19 | 8% |
Biochemistry, Genetics and Molecular Biology | 9 | 4% |
Other | 21 | 9% |
Unknown | 43 | 19% |