Title | ||
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Statistical and Deformable Model Approaches to the Segmentation of MR Imagery and Volume Estimation of Stroke Lesions |
Abstract | ||
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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. |
Year | DOI | Venue |
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2001 | 10.1007/3-540-45468-3_99 | MICCAI |
Keywords | Field | DocType |
shape-based information,stroke lesions,deformable model,intensity-based information,mr imagery,deformable models framework,regularized method,hybrid approach,ischemic stroke patient,lesion volume,stroke therapy,deformable model approaches,volume estimation,statistical method,confidence interval | Active contour model,Computer vision,Pattern recognition,Lesion,Computer science,Segmentation,Stroke,Artificial intelligence,Volume estimation,Initialization,Confidence interval,Magnetic resonance imaging | Conference |
ISBN | Citations | PageRank |
3-540-42697-3 | 1 | 0.66 |
References | Authors | |
9 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Benjamin Stein | 1 | 1 | 1.00 |
Dimitri Lisin | 2 | 5 | 1.12 |
Joseph Horowitz | 3 | 1 | 0.66 |
E. M. Riseman | 4 | 1402 | 458.95 |
Gary Whitten | 5 | 1 | 0.66 |