PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT release_nc7sgduxjfbdfln6mknl7mr7py

by John Muschelli, Elizabeth M. Sweeney, Natalie L. Ullman, Paul Vespa, Daniel F. Hanley, Ciprian M. Crainiceanu

Published in NeuroImage: Clinical by Elsevier BV.

2017   Volume 14, p379-390

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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Type  article-journal
Stage   published
Date   2017-02-15
Language   en ?
DOI  10.1016/j.nicl.2017.02.007
PubMed  28275541
PMC  PMC5328741
Wikidata  Q37670176
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