Share Email Print
cover

Proceedings Paper

Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis
Author(s): Maysam Shahedi; Aaron Fenster; Derek W. Cool; Cesare Romagnoli; Aaron D. Ward
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays – one corresponding to each of the mean intensity patches computed in training – emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean±std MAD of 2.5±0.7 mm, DSC of 80±4%, and ΔV of 1.1±8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.

Paper Details

Date Published: 12 March 2013
PDF: 6 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710N (12 March 2013); doi: 10.1117/12.2006465
Show Author Affiliations
Maysam Shahedi, Baines Ctr. for Translational Cancer Research, London Health Science Ctr. (Canada)
Robarts Research Institute, The Univ. of Western Ontario (Canada)
Biomedical Engineering Graduate Program, The Univ. of Western Ontario (Canada)
Aaron Fenster, Robarts Research Institute, The Univ. of Western Ontario (Canada)
Biomedical Engineering Graduate Program, , The Univ. of Western Ontario (Canada)
The Univ. of Western Ontario
Derek W. Cool, Robarts Research Institute, The Univ. of Western Ontario (Canada)
Cesare Romagnoli, Robarts Research Institute, The Univ. of Western Ontario (Canada)
Aaron D. Ward, Baines Ctr. for Translational Cancer Research, London Health Science Ctr. (Canada)
Biomedical Engineering Graduate Program, The Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

© SPIE. Terms of Use
Back to Top