Statistical and Deformable Model Approaches to the Segmentation of MR Imagery and Volume Estimation of Stroke Lesions

Authors:
Benjamin Stein;Dimitri Lisin;Joseph Horowitz;Edward M. Riseman;Gary Whitten
Affiliations:
-;-;-;-;-
Venue:
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Year:
2001

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Abstract

We propose two 3D methods to segment magnetic resonance imagery (MRI) of ischemic stroke patients into lesion and background, and hence to estimate lesion volumes. The first is a hierarchical, regularized method based on classical statistics that produces a rigorous confidence interval for lesion volume. This approach requires a limited amount of user interaction to initialize, but this step can be time-consuming. The second method integrates the first into the deformable models framework. This hybrid approach combines intensity-based information provided by the statistical method and shape-based information given by the deformable model. It also requires less initialization than the statistical method. Both procedures have been tested on real MR data, with volume estimates within 20% of those derived from doctors' hand segmentations. According to the physicians with whom we are working, these results are clinically useful to evaluate stroke therapies.