Abstract
We combine nonlinear diffusion scale-space and geometric deformable models for segmenting lesions in MR images of ischemic stroke patients. Region and boundary information are integrated in a speed function for robust segmentation with the fast marching level set method. A confidence-based model of segmentation captures the significant variability in human segmentation and the ambiguity inherent in many lesions, and it provides a testbed for a new measure of variance with sets as random variables. This method offers users a family of segmentations, requires less user input than previous methods, and its volume estimates effectively match those of doctors’ hand segmentations.
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Weinman, J., Bissias, G., Horowitz, J., Riseman, E., Hanson, A. (2003). Nonlinear Diffusion Scale-Space and Fast Marching Level Sets for Segmentation of MR Imagery and Volume Estimation of Stroke Lesions. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_61
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DOI: https://doi.org/10.1007/978-3-540-39903-2_61
Publisher Name: Springer, Berlin, Heidelberg
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