title: Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage.
abstract: OBJECTIVE
  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.
authors.affiliation: 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.
authors.forename: Moritz
authors.initials: M
authors.lastname: Scherer
authors.affiliation: 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.).
authors.forename: Jonas
authors.initials: J
authors.lastname: Cordes
authors.affiliation: 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.).
authors.forename: Alexander
authors.initials: A
authors.lastname: Younsi
authors.affiliation: 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.).
authors.forename: Yasemin-Aylin
authors.initials: YA
authors.lastname: Sahin
authors.affiliation: 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.).
authors.forename: Michael
authors.initials: M
authors.lastname: Götz
authors.affiliation: 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.).
authors.forename: Markus
authors.initials: M
authors.lastname: Möhlenbruch
authors.affiliation: 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.).
authors.forename: Christian
authors.initials: C
authors.lastname: Stock
authors.affiliation: 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.).
authors.forename: Julian
authors.initials: J
authors.lastname: Bösel
authors.affiliation: 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.).
authors.forename: Andreas
authors.initials: A
authors.lastname: Unterberg
authors.affiliation: 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.).
authors.forename: Klaus
authors.initials: K
authors.lastname: Maier-Hein
authors.affiliation: 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.).
authors.forename: Berk
authors.initials: B
authors.lastname: Orakcioglu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002543:Cerebral Hemorrhage / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000592:standards
keywords: computed tomography; computer-assisted image analysis; intracerebral hemorrhage; machine learning; volumetric analysis