PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT
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John Muschelli, Elizabeth M. Sweeney, Natalie L. Ullman, Paul Vespa, Daniel F. Hanley, Ciprian M. Crainiceanu
Abstract
Intracerebral hemorrhage (ICH), where a blood vessel ruptures into areas of the brain, accounts for approximately 10-15% of all strokes. X-ray computed tomography (CT) scanning is largely used to assess the location and volume of these hemorrhages. Manual segmentation of the CT scan using planimetry by an expert reader is the gold standard for volume estimation, but is time-consuming and has within- and across-reader variability. We propose a fully automated segmentation approach using a random forest algorithm with features extracted from X-ray computed tomography (CT) scans.
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