title: A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images.
abstract: Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91 ± 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67 ± 0.02 s.
authors.affiliation: Australian e-Health Research Centre, CSIRO, Brisbane, QLD 4029, Australia. soumya.ghose@csiro.au
authors.forename: Soumya
authors.initials: S
authors.lastname: Ghose
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.forename: Jhimli
authors.initials: J
authors.lastname: Mitra
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.forename: Désiré
authors.initials: D
authors.lastname: Sidibé
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.forename: Josep
authors.initials: J
authors.lastname: Comet
authors.forename: Fabrice
authors.initials: F
authors.lastname: Meriaudeau
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*