title: A pattern recognition approach to zonal segmentation of the prostate on MRI.
abstract: Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 +/- 0.03 for the central gland and 0.75 +/- 0.07 for the peripheral zone, compared to 0.87 +/- 0.04 and 0.76 +/- 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate.
authors.affiliation: Radboud University Nijmegen Medical Centre, Geert Grootteplein-Zuid 10, 6525GA Nijmegen, The Netherlands.
authors.forename: Geert
authors.initials: G
authors.lastname: Litjens
authors.forename: Oscar
authors.initials: O
authors.lastname: Debats
authors.forename: Wendy
authors.initials: W
authors.lastname: van de Ven
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.forename: Henkjan
authors.initials: H
authors.lastname: Huisman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity