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Proceedings Paper

Computer aided analysis of prostate histopathology images to support a refined Gleason grading system
Author(s): Jian Ren; Evita Sadimin; David J. Foran; Xin Qi
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Paper Abstract

The Gleason grading system used to render prostate cancer diagnosis has recently been updated to allow more accurate grade stratification and higher prognostic discrimination when compared to the traditional grading system. In spite of progress made in trying to standardize the grading process, there still remains approximately a 30% grading discrepancy between the score rendered by general pathologists and those provided by experts while reviewing needle biopsies for Gleason pattern 3 and 4, which accounts for more than 70% of daily prostate tis- sue slides at most institutions. We propose a new computational imaging method for Gleason pattern 3 and 4 classification, which better matches the newly established prostate cancer grading system. The computer- aided analysis method includes two phases. First, the boundary of each glandular region is automatically segmented using a deep convolutional neural network. Second, color, shape and texture features are extracted from superpixels corresponding to the outer and inner glandular regions and are subsequently forwarded to a random forest classifier to give a gradient score between 3 and 4 for each delineated glandular region. The F1 score for glandular segmentation is 0.8460 and the classification accuracy is 0.83±0.03.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331V (24 February 2017); doi: 10.1117/12.2253887
Show Author Affiliations
Jian Ren, Rutgers, The State Univ. of New Jersey (United States)
Evita Sadimin, Cancer Institute of New Jersey, Rutgers, The State Univ. of New Jersey (United States)
David J. Foran, Cancer Institute of New Jersey, Rutgers, The State Univ. of New Jersey (United States)
Xin Qi, Cancer Institute of New Jersey, Rutgers, The State Univ. of New Jersey (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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