Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis.
Publication year
2012Source
Physics in Medicine and Biology, 57, 6, (2012), pp. 1527-42ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Radiology
Data Science
Journal title
Physics in Medicine and Biology
Volume
vol. 57
Issue
iss. 6
Page start
p. 1527
Page end
p. 42
Subject
Data Science; NCMLS 4: Energy and redox metabolism ONCOL 5: Aetiology, screening and detection; ONCOL 5: Aetiology, screening and detectionAbstract
In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.
This item appears in the following Collection(s)
- Academic publications [238441]
- Electronic publications [122523]
- Faculty of Medical Sciences [90373]
- Faculty of Science [34986]
- Open Access publications [97518]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.