Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.
Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evalua...
| Published in: | Journal of Digital Imaging Vol. 30; no. 6; pp. 782 - 796 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas tables/charts Journal Article |
| Published: |
Springer Nature
Dec2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126169915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126169915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2017 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 126169915 126169915 144146652 126169915 10.1007/s10278-017-9964-7 126169915 ppf: 782 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging. aug: au: Shahedi, Maysam Cool, Derek Bauman, Glenn Bastian-Jordan, Matthew Fenster, Aaron Ward, Aaron affil: The Department of Medical Imaging , The University of Western Ontario , London Canada sug: subj: Prostatic Neoplasms Digital Imaging Magnetic Resonance Imaging Image Enhancement Methods Matched-Pair Analysis Prostate Image Processing, Computer Assisted Methods Algorithms Validity Evaluation ab: Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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