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System and method for unsupervised detection and gleason grading of prostate cancer whole mounts using NIR fluorscence

  • US 8,139,831 B2
  • Filed: 12/02/2008
  • Issued: 03/20/2012
  • Est. Priority Date: 12/06/2007
  • Status: Active Grant
First Claim
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1. A method for unsupervised classification of histological images of prostatic tissue, comprising the steps of:

  • providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&

    E) stains;

    segmenting prostate gland units in the image data;

    forming feature vectors by computing discriminating attributes of the segmented gland units; and

    using said feature vectors to train a multi-class classifier within a Bayesian framework, wherein said classifier is arranged to classify prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories and to use Bayesian posterior probabilities to determine a strength of a diagnosis, wherein a borderline prognosis between two categories is provided to a second phase classifier using a classification model whose parameters are tuned to the two categories of the borderline prognosis.

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