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Proceedings Paper

Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach
Author(s): G. J. S. Litjens; J. O. Barentsz; N. Karssemeijer; H. J. Huisman
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Paper Abstract

MRI has shown to have great potential in prostate cancer localization and grading, but interpreting those exams requires expertise that is not widely available. Therefore, CAD applications are being developed to aid radiologists in detecting prostate cancer. Existing CAD applications focus on the prostate as a whole. However, in clinical practice transition zone cancer and peripheral zone cancer are considered to have different appearances. In this paper we present zone-specific CAD, in addition to an atlas based segmentation technique which includes zonal segmentation. Our CAD system consists of a detection and a classification stage. Prior to the detection stage the prostate is segmented into two zones. After segmentation features are extracted. Subsequently a likelihood map is generated on which local maxima detection is performed. For each local maximum a region is segmented. In the classification stage additional shape features are calculated, after which the regions are classified. Validation was performed on 288 data sets with MR-guided biopsy results as ground truth. Freeresponse Receiver Operating Characteristic (FROC) analysis was used for statistical evaluation. The difference between whole-prostate and zone-specific CAD was assessed using the difference between the FROCs. Our results show that evaluating the two zones separately results in an increase in performance compared to whole-prostate CAD. The FROC curves at .1, 1 and 3 false positives have a sensitivity of 0.0, 0.55 and 0.72 for whole-prostate and 0.08, 0.57 and 0.80 for zone-specific CAD. The FROC curve of the zone-specific CAD also showed significantly better performance overall (p < 0.05).

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150G (23 February 2012); doi: 10.1117/12.911061
Show Author Affiliations
G. J. S. Litjens, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
J. O. Barentsz, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
N. Karssemeijer, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
H. J. Huisman, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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