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Proceedings Paper

2D registration guided models for semi-automatic MRI prostate segmentation
Author(s): Ruida Cheng; Baris Turkbey; Justin Senseney; Marcelino Bernardo; Alexandra Bokinsky; William Gandler; Evan McCreedy; Thomas Pohida; Peter Choyke; Matthew J. McAuliffe
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Paper Abstract

Accurate segmentation of prostate magnetic resonance images (MRI) is a challenging task due to the variable anatomical structure of the prostate. In this work, two semi-automatic techniques for segmentation of T2-weighted MRI images of the prostate are presented. Both models are based on 2D registration that changes shape to fit the prostate boundary between adjacent slices. The first model relies entirely on registration to segment the prostate. The second model applies Fuzzy-C means and morphology filters on top of the registration in order to refine the prostate boundary. Key to the success of the two models is the careful initialization of the prostate contours, which requires specifying three Volume of Interest (VOI) contours to each axial, sagittal and coronal image. Then, a fully automatic segmentation algorithm generates the final results with the three images. The algorithm performance is evaluated with 45 MR image datasets. VOI volume, 3D surface volume and VOI boundary masks are used to quantify the segmentation accuracy between the semi-automatic and expert manual segmentations. Both models achieve an average segmentation accuracy of 90%. The proposed registration guided segmentation model has been generalized to segment a wide range of T2- weighted MRI prostate images.

Paper Details

Date Published: 13 March 2013
PDF: 10 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692V (13 March 2013); doi: 10.1117/12.2006225
Show Author Affiliations
Ruida Cheng, National Institutes of Health (United States)
Baris Turkbey, National Cancer Institute (United States)
Justin Senseney, National Institutes of Health (United States)
Marcelino Bernardo, National Cancer Institute (United States)
Frederick National Lab. (United States)
Alexandra Bokinsky, Geometric Tools, Inc. (United States)
William Gandler, National Institutes of Health (United States)
Evan McCreedy, National Institutes of Health (United States)
Thomas Pohida, National Institutes of Health (United States)
Peter Choyke, National Cancer Institute (United States)
Matthew J. McAuliffe, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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