Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

Maysam Shahedi; Aaron Fenster; Derek W. Cool; Cesare Romagnoli; Aaron D. Ward

doi:10.1117/12.2006465

12 March 2013 Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

Maysam Shahedi, Aaron Fenster, Derek W. Cool, Cesare Romagnoli, Aaron D. Ward

Author Affiliations +

Proceedings Volume 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling; 86710N (2013) https://doi.org/10.1117/12.2006465
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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, Aaron Fenster, Derek W. Cool, Cesare Romagnoli, and 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); https://doi.org/10.1117/12.2006465

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KEYWORDS

Image segmentation

Prostate

Magnetic resonance imaging

3D image processing

3D modeling

Cancer

Data modeling

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