15 May 2003 Automatic model-based 3D lesion segmentation for evaluation of MR-guided thermal ablation therapy
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We are investigating magnetic resosance imaging-guided radiofrequency ablation of pathologic tissue. For many tissues, resulting lesions have a characteristic two-boundary appearance featuring an inner region and an outer hyper-intense margin in both contrast enchanced (CE) T1 and T2 weighted MR images. We created a twelve-parameter, three-dimensional, globally deformable model with two quadratic surfaces that describe both lesion zones. We present an energy minimization approach to automatically fit the model to a grayscale MR image volume. We applied the automatic model to in vivo lesions (n = 5) in a rabbit thigh model, using CE T1 and T2 weighted MR images, and compared the results to multi-operator manually segmented boundaries. For all lesions, the median error was <1.0mm for both the inner and outer regions, values that favorably compare to a voxel width of 0.7 mm. These results suggest that our method provides a precise, automatic approximation of lesion shape. We believe that the method has applications in lesion visualization, volume estimation, image quantification, and volume registration.
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Roee S Lazebnik, Brent D Weinberg, Michael S Breen, Jonathan S. Lewin, and David L. Wilson "Automatic model-based 3D lesion segmentation for evaluation of MR-guided thermal ablation therapy", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481385; https://doi.org/10.1117/12.481385

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