12 March 2013 Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis
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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.
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Maysam Shahedi, Maysam Shahedi, Aaron Fenster, Aaron Fenster, Derek W. Cool, Derek W. Cool, Cesare Romagnoli, Cesare Romagnoli, Aaron D. Ward, Aaron D. Ward, } "Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710N (12 March 2013); doi: 10.1117/12.2006465; https://doi.org/10.1117/12.2006465
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