title: Incremental shape statistics learning for prostate tracking in TRUS.
abstract: Automatic delineation of the prostate boundary in transrectal ultrasound (TRUS) can play a key role in image-guided prostate intervention. However, it is a very challenging task for several reasons, especially due to the large variation of the prostate shape from the base to the apex. To deal with the problem, a new method for incrementally learning the patient-specific local shape statistics is proposed in this paper to help achieve robust and accurate boundary delineation over the entire prostate gland. The proposed method is fast and memory efficient in that new shapes can be merged into the shape statistics without recomputing using all the training shapes, which makes it suitable for use in real-time interventional applications. In our work, the learned shape statistics is incorporated into a modified sequential inference model for tracking the prostate boundary. Experimental results show that the proposed method is more robust and accurate than the active shape model using global population-based shape statistics in delineating the prostate boundary in TRUS.
authors.affiliation: Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA.
authors.forename: Pingkun
authors.initials: P
authors.lastname: Yan
authors.forename: Jochen
authors.initials: J
authors.lastname: Kruecker
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography