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Proceedings Paper

Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens
Author(s): G. Litjens; B. Ehteshami Bejnordi; N. Timofeeva; G. Swadi; I. Kovacs; C. Hulsbergen-van de Kaa; J. van der Laak
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Paper Abstract

Automated detection of prostate cancer in digitized H and E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H and E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity.

Paper Details

Date Published: 19 March 2015
PDF: 6 pages
Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200B (19 March 2015); doi: 10.1117/12.2081366
Show Author Affiliations
G. Litjens, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
B. Ehteshami Bejnordi, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
N. Timofeeva, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
G. Swadi, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
I. Kovacs, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
C. Hulsbergen-van de Kaa, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
J. van der Laak, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 9420:
Medical Imaging 2015: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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