Nonlinear diffusion scale-space and fast marching level sets for segmentation of MR imagery and volume estimation of stroke lesions

Abstract

We comuine 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.