title: Automated Region-based Prostate Cancer Cell Nuclei Localization. Part of a Prognostic Modality Tool for Prostate Cancer Patients.
abstract: BACKGROUND
  Prostate cancer is a disease of disrupted cell genomes. Quantification of DNA from cytology preparations can yield prognostic information about tissue biological behaviors; however, this process is very labor-intensive to perform. Quantitative digital pathology can measure the structural chromatin changes associated with neoplasia and can enable prognostic and predictive assays based on imaging of sectioned prostate tissue.
  
  
  OBJECTIVE
  To design an automated system to recognize and localize cell nuclei in images of stained sectioned tissue (first step in enabling quantitative digital pathology).
  
  
  METHODS
  Images of Feulgen-thionin-stained prostate cancer tissue microarray constructed from the surgical specimens of 33 prostate cancer patients were acquired for this study. We implemented a new image segmentation technique to overcome tissue complexity, cell clusters, background noise, image and tissue inhomogeneities, and other imaging issues that introduce uncertainties into the segmentation method and developed a fully automated system to localized prostate cell nuclei.
  
  
  RESULTS
  We applied our algorithm on our dataset and obtained a 96.6% true-positive rate and a 12% false-positive rate.
  
  
  CONCLUSIONS
  In this paper we present a new method to automatically localize thionin-stained prostate cancer cells, enabling the extraction of various nuclear and cell sociology features with high precision.
authors.forename: Nilgoon
authors.initials: N
authors.lastname: Zarei
authors.forename: Amir
authors.initials: A
authors.lastname: Bakhtiari
authors.forename: Jagoda
authors.initials: J
authors.lastname: Korbelik
authors.forename: Anita
authors.initials: A
authors.lastname: Carraro
authors.forename: Mira
authors.initials: M
authors.lastname: Keyes
authors.forename: Martial
authors.initials: M
authors.lastname: Guillaud
authors.forename: Calum
authors.initials: C
authors.lastname: MacAulay
mesh_terms: D000465:Algorithms; D057205:Automation, Laboratory; D002467:Cell Nucleus / Q000737:chemistry / Q000473:pathology*; D004396:Coloring Agents; D004247:DNA / Q000032:analysis; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D011237:Predictive Value of Tests; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000737:chemistry / Q000473:pathology* / Q000601:surgery; D015203:Reproducibility of Results; D012394:Rosaniline Dyes; D013194:Staining and Labeling / Q000379:methods*; D054809:Thionins; D046888:Tissue Array Analysis
chemical_list: D004396:Coloring Agents; C007722:Feulgen stain; D012394:Rosaniline Dyes; D054809:Thionins; D004247:DNA