Development and validation of an automatic segmentation algorithm for quantification of intracerebral hemorrhage
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 algo...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
August 29, 2016
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| In: |
Stroke
Year: 2016, Volume: 47, Issue: 11, Pages: 2776-2782 |
| ISSN: | 1524-4628 |
| DOI: | 10.1161/STROKEAHA.116.013779 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1161/STROKEAHA.116.013779 Verlag, kostenfrei, Volltext: http://stroke.ahajournals.org/content/47/11/2776 
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| Author Notes: | Moritz Scherer, MD; Jonas Cordes, MSc; Alexander Younsi, MD; Yasemin-Aylin Sahin, MS; Michael Götz, MSc; Markus Möhlenbruch, MD; Christian Stock, MSc, PhD; Julian Bösel, MD; Andreas Unterberg, MD, PhD; Klaus Maier-Hein, MSc, PhD; Berk Orakcioglu, MD |
| Summary: | 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. |
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| Item Description: | Gesehen am 01.12.2017 |
| Physical Description: | Online Resource |
| ISSN: | 1524-4628 |
| DOI: | 10.1161/STROKEAHA.116.013779 |