title: Detecting and segmenting cell nuclei in two-dimensional microscopy images.
abstract: BACKGROUND
  Cell nuclei are important indicators of cellular processes and diseases. Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted from microscopy images. Given the wide variety of nuclei appearance in different organs and staining procedures, a plethora of methods have been described in the literature to improve the segmentation accuracy and robustness.
  
  
  METHODS
  In this paper, we propose an unsupervised method for cell nuclei detection and segmentation in two-dimensional microscopy images. The nuclei in the image are detected automatically using a matching-based method. Next, edge maps are generated at multiple image blurring levels followed by edge selection performed in polar space. The nuclei contours are refined iteratively in the constructed edge pyramid. The validation study was conducted over two cell nuclei datasets with manual labeling, including 25 hematoxylin and eosin-stained liver histopathology images and 35 Papanicolaou-stained thyroid images.
  
  
  RESULTS
  The nuclei detection accuracy was measured by miss rate, and the segmentation accuracy was evaluated by two types of error metrics. Overall, the nuclei detection efficiency of the proposed method is similar to the supervised template matching method. In comparison to four existing state-of-the-art segmentation methods, the proposed method performed the best with average segmentation error 10.34% and 0.33 measured by area error rate and normalized sum of distances (×10).
  
  
  CONCLUSIONS
  Quantitative analysis showed that the method is automatic and accurate when segmenting cell nuclei from microscopy images with noisy background and has the potential to be used in clinic settings.
authors.affiliation: Department of Biomedical Engineering, Carnegie Mellon University, Beijing, China.
authors.forename: Chi
authors.initials: C
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, Beijing Institute of Technology, Beijing, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Shang
authors.affiliation: Department of Pathology, Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Ozolek
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA.
authors.forename: Gustavo K
authors.initials: GK
authors.lastname: Rohde
keywords: Cell nuclei detection and segmentation; multiscale method; pathology images