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Stroke. 2016 Nov;47(11):2776-2782. Epub 2016 Oct 4.

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage.

Author information

1
From the Department of Neurosurgery (M.S., A.Y., Y.-A.S., A.U., B.O.), Institute of Medical Biometry and Informatics (IMBI) (C.S.), and Department of Neurology (J.B.), University Hospital Heidelberg, Germany; Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.C., M.G., K.M.-H.); and Division of Neuroradiology, Heidelberg University Hospital, Germany (M.M.). moritz.scherer@med.uni-heidelberg.de.
2
From the Department of Neurosurgery (M.S., A.Y., Y.-A.S., A.U., B.O.), Institute of Medical Biometry and Informatics (IMBI) (C.S.), and Department of Neurology (J.B.), University Hospital Heidelberg, Germany; Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.C., M.G., K.M.-H.); and Division of Neuroradiology, Heidelberg University Hospital, Germany (M.M.).

Abstract

BACKGROUND AND PURPOSE:

ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of ICH.

METHODS:

A segmentation algorithm implementing first- and second-order statistics, texture, and threshold features was trained on manual segmentations with a random-forest methodology. Quantitative data of the algorithm, manual segmentations, and ABC/2 were evaluated for agreement in a study sample (n=28) and validated in an independent sample not used for algorithm training (n=30).

RESULTS:

ABC/2 volumes were significantly larger compared with either manual or algorithm values, whereas no significant differences were found between the latter (P<0.0001; Friedman+Dunn's multiple comparison). Algorithm agreement with the manual reference was strong (concordance correlation coefficient 0.95 [lower 95% confidence interval 0.91]) and superior to ABC/2 (concordance correlation coefficient 0.77 [95% confidence interval 0.64]). Validation confirmed agreement in an independent sample (algorithm concordance correlation coefficient 0.99 [95% confidence interval 0.98], ABC/2 concordance correlation coefficient 0.82 [95% confidence interval 0.72]). The algorithm was closer to respective manual segmentations than ABC/2 in 52/58 cases (89.7%).

CONCLUSIONS:

An automatic segmentation algorithm for volumetric analysis of spontaneous ICH was developed and validated in this study. Algorithm measurements showed strong agreement with manual segmentations, whereas ABC/2 exhibited its limitations, yielding inaccurate overestimations of ICH volume. The refined, yet time-efficient, quantification of ICH by the algorithm may facilitate evaluation of clot volume as an outcome predictor and trigger for surgical interventions in the clinical setting.

KEYWORDS:

computed tomography; computer-assisted image analysis; intracerebral hemorrhage; machine learning; volumetric analysis

PMID:
27703089
DOI:
10.1161/STROKEAHA.116.013779
[Indexed for MEDLINE]

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