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Proceedings Paper

Automatic model-based 3D lesion segmentation for evaluation of MR-guided thermal ablation therapy
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Paper Abstract

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.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481385
Show Author Affiliations
Roee S Lazebnik, Case Western Reserve Univ. (United States)
Brent D Weinberg, Case Western Reserve Univ. (United States)
Michael S Breen, Case Western Reserve Univ. (United States)
Jonathan S. Lewin, Univ. Hospitals of Cleveland (United States)
David L. Wilson, Case Western Reserve Univ. (United States)
University Hospitals of Cleveland (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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