title: Automated cerebral infarct volume measurement in follow-up noncontrast CT scans of patients with acute ischemic stroke.
abstract: OBJECTIVE
  Cerebral infarct volume as observed in follow-up CT is an important radiologic outcome measure of the effectiveness of treatment of patients with acute ischemic stroke. However, manual measurement of CIV is time-consuming and operator-dependent. The purpose of this study was to develop and evaluate a robust automated measurement of the CIV.
  
  
  METHODS
  The CIV in early follow-up CT images of 34 consecutive patients with acute ischemic stroke was segmented with an automated intensity-based region-growing algorithm, which includes partial volume effect correction near the skull, midline determination, and ventricle and hemorrhage exclusion. Two observers manually delineated the CIV. Interobserver variability of the manual assessments and the accuracy of the automated method were evaluated by using the Pearson correlation, Bland-Altman analysis, and Dice coefficients. The accuracy was defined as the correlation with the manual assessment as a reference standard.
  
  
  RESULTS
  The Pearson correlation for the automated method compared with the reference standard was similar to the manual correlation (R = 0.98). The accuracy of the automated method was excellent with a mean difference of 0.5 mL with limits of agreement of -38.0-39.1 mL, which were more consistent than the interobserver variability of the 2 observers (-40.9-44.1 mL). However, the Dice coefficients were higher for the manual delineation.
  
  
  CONCLUSIONS
  The automated method showed a strong correlation and accuracy with the manual reference measurement. This approach has the potential to become the standard in assessing the infarct volume as a secondary outcome measure for evaluating the effectiveness of treatment.
authors.affiliation: Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: A M
authors.initials: AM
authors.lastname: Boers
authors.forename: H A
authors.initials: HA
authors.lastname: Marquering
authors.forename: J J
authors.initials: JJ
authors.lastname: Jochem
authors.forename: N J
authors.initials: NJ
authors.lastname: Besselink
authors.forename: O A
authors.initials: OA
authors.lastname: Berkhemer
authors.forename: A
authors.initials: A
authors.lastname: van der Lugt
authors.forename: L F
authors.initials: LF
authors.lastname: Beenen
authors.forename: C B
authors.initials: CB
authors.lastname: Majoie
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D001185:Artificial Intelligence; D002545:Brain Ischemia / Q000150:complications / Q000000981:diagnostic imaging*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009426:Netherlands; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020521:Stroke / Q000000981:diagnostic imaging* / Q000209:etiology; D014057:Tomography, X-Ray Computed / Q000379:methods*