Title
Statistical and Deformable Model Approaches to the Segmentation of MR Imagery and Volume Estimation of Stroke Lesions
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.
Year
DOI
Venue
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 Stein111.00
Dimitri Lisin251.12
Joseph Horowitz310.66
E. M. Riseman41402458.95
Gary Whitten510.66