title: Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes.
abstract: Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62% (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90%). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.
authors.affiliation: Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.|Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.
authors.forename: Farzad
authors.initials: F
authors.lastname: Khalvati
authors.affiliation: Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada.
authors.forename: Aryan
authors.initials: A
authors.lastname: Salmanpour
authors.affiliation: Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada.
authors.forename: Shahryar
authors.initials: S
authors.lastname: Rahnamayan
authors.affiliation: Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.|Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada.
authors.forename: Masoom A
authors.initials: MA
authors.lastname: Haider
authors.affiliation: Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada.
authors.forename: H R
authors.initials: HR
authors.lastname: Tizhoosh
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008297:Male; D009929:Organ Size; D011467:Prostate / Q000000981:diagnostic imaging* / Q000473:pathology
keywords: Image registration; Magnetic resonance imaging; Prostate segmentation; Prostate volume