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). STUDY DESIGN: 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. CONCLUSION: 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.
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). STUDY DESIGN: Images of Feulgen-thionin-stained prostate cancer tissue microarray constructed from the surgical specimens of 33 prostate cancerpatients 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. CONCLUSION: 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.