13 March 2013 2D registration guided models for semi-automatic MRI prostate segmentation
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86692V (2013) https://doi.org/10.1117/12.2006225
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
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.
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Ruida Cheng, Ruida Cheng, Baris Turkbey, Baris Turkbey, Justin Senseney, Justin Senseney, Marcelino Bernardo, Marcelino Bernardo, Alexandra Bokinsky, Alexandra Bokinsky, William Gandler, William Gandler, Evan McCreedy, Evan McCreedy, Thomas Pohida, Thomas Pohida, Peter Choyke, Peter Choyke, Matthew J. McAuliffe, Matthew J. McAuliffe, } "2D registration guided models for semi-automatic MRI prostate segmentation", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692V (13 March 2013); doi: 10.1117/12.2006225; https://doi.org/10.1117/12.2006225
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