Abstract T P45: Automated CSF Segmentation to Quantify Cerebral Edema Following Large Hemispheric Ischemic Stroke

Abstract

Introduction: Cerebral edema with resultant mass effect is a potentially fatal consequence of ischemic stroke, but early and sensitive biomarkers of brain tissue compression are lacking. To quantify brain mass effect, we developed a novel, automated segmentation method to delineate CSF spaces in CT images from ischemic stroke patients. Methods: CTs from sixteen acute ischemic stroke patients (median NIHSS 16.5, median age 61.5 yrs, 14-92 hrs after stroke onset) were included after informed consent was obtained. After infarction, conventional CSF segmentation using Hounsfield unit (HU) thresholding is suboptimal due to infarct hypodensity. Utilizing manually delineated infarct and CSF spaces as training samples, we augmented conventional HU threshold segmentation with level sets, sparse regression and random forest segmentation methods. Using leave-one-out cross-validation, the combined approach was compared to HU thresholding using Dice ratios (a measure of the overlap between the segmented and the ground-truth CSF spaces). Results: Shown is an example of a CT brain slice segmented by HU thresholding and the combined strategy: false negative (red), false positive (green), and true positive (yellow). The Dice ratios for HU thresholding and the combined approaches were 58.2±16.3% and 68.9±14.6%, respectively, demonstrating the significantly improved performance for the combined strategy (p=0.0014). Conclusions: We have developed an advanced image segmentation strategy to delineate CSF spaces which outperforms conventional HU thresholding. An automated CSF segmentation strategy will permit quantification of cerebral edema in a large population of stroke patients, as required for genetic studies, for example.