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Neuroimage Clin. 2016 Sep 26;12:673-680. eCollection 2016.

Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs.

Author information

1
Department of Neurology, Washington University, St. Louis, MO 63110, USA.
2
Emergency Medicine, Washington University, St. Louis, MO 63110, USA.
3
Stroke Pharmacogenomics and Genetics, Fundacio Docencia i Recerca MutuaTerrassa, Mutua de Terrassa Hospital, Terrassa, Barcelona, Spain; Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Universitat Autonoma de Barcelona, Barcelona, Spain.
4
Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Universitat Autonoma de Barcelona, Barcelona, Spain.
5
Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA; Dept. of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA.
6
Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA; Dept. of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
7
Radiology, Washington University, St. Louis, MO 63110, USA.
8
Department of Neurology, Washington University, St. Louis, MO 63110, USA; Radiology, Washington University, St. Louis, MO 63110, USA; Biomedical Engineering, Washington University, St. Louis, MO 63110, USA.

Abstract

Although cerebral edema is a major cause of death and deterioration following hemispheric stroke, there remains no validated biomarker that captures the full spectrum of this critical complication. We recently demonstrated that reduction in intracranial cerebrospinal fluid (CSF) volume (∆ CSF) on serial computed tomography (CT) scans provides an accurate measure of cerebral edema severity, which may aid in early triaging of stroke patients for craniectomy. However, application of such a volumetric approach would be too cumbersome to perform manually on serial scans in a real-world setting. We developed and validated an automated technique for CSF segmentation via integration of random forest (RF) based machine learning with geodesic active contour (GAC) segmentation. The proposed RF + GAC approach was compared to conventional Hounsfield Unit (HU) thresholding and RF segmentation methods using Dice similarity coefficient (DSC) and the correlation of volumetric measurements, with manual delineation serving as the ground truth. CSF spaces were outlined on scans performed at baseline (< 6 h after stroke onset) and early follow-up (FU) (closest to 24 h) in 38 acute ischemic stroke patients. RF performed significantly better than optimized HU thresholding (p < 10- 4 in baseline and p < 10- 5 in FU) and RF + GAC performed significantly better than RF (p < 10- 3 in baseline and p < 10- 5 in FU). Pearson correlation coefficients between the automatically detected ∆ CSF and the ground truth were r = 0.178 (p = 0.285), r = 0.876 (p < 10- 6) and r = 0.879 (p < 10- 6) for thresholding, RF and RF + GAC, respectively, with a slope closer to the line of identity in RF + GAC. When we applied the algorithm trained from images of one stroke center to segment CTs from another center, similar findings held. In conclusion, we have developed and validated an accurate automated approach to segment CSF and calculate its shifts on serial CT scans. This algorithm will allow us to efficiently and accurately measure the evolution of cerebral edema in future studies including large multi-site patient populations.

KEYWORDS:

Active contour; CSF segmentation; Cerebral edema; Ischemic stroke CT; Mass effect; Random forest

PMID:
27761398
PMCID:
PMC5065050
DOI:
10.1016/j.nicl.2016.09.018
[Indexed for MEDLINE]
Free PMC Article

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