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EVALUATION STUDIES
JOURNAL ARTICLE
Automated Region-based Prostate Cancer Cell Nuclei Localization. Part of a Prognostic Modality Tool for Prostate Cancer Patients.
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.
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.
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