Journal
SCIENTIFIC REPORTS
Volume 2, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep00503
Keywords
-
Categories
Funding
- Charite Medical School Berlin
- Human Frontier Science Program (HFSP) Young Investigator Grant [RGY0077/2011]
Ask authors/readers for more resources
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 similar to 8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available