title: Automated posterior cranial fossa volumetry by MRI: applications to Chiari malformation type I.
abstract: OBJECTIVE
  Quantification of PCF volume and the degree of PCF crowdedness were found beneficial for differential diagnosis of tonsillar herniation and prediction of surgical outcome in CMI. However, lack of automated methods limits the clinical use of PCF volumetry. An atlas-based method for automated PCF segmentation tailored for CMI is presented. The method performance is assessed in terms of accuracy and spatial overlap with manual segmentation. The degree of association between PCF volumes and the lengths of previously proposed linear landmarks is reported.
  
  
  METHODS
  T1-weighted volumetric MR imaging data with 1-mm isotropic resolution obtained with the use of a 3T scanner from 14 patients with CMI and 3 healthy subjects were used for the study. Manually delineated PCF from 9 patients was used to establish a CMI-specific reference for an atlas-based automated PCF parcellation approach. Agreement between manual and automated segmentation of 5 different CMI datasets was verified by means of the t test. Measurement reproducibility was established through the use of 2 repeated scans from 3 healthy subjects. Degree of linear association between PCF volume and 6 linear landmarks was determined by means of Pearson correlation.
  
  
  RESULTS
  PCF volumes measured by use of the automated method and with manual delineation were similar, 196.2 ± 8.7 mL versus 196.9 ± 11.0 mL, respectively. The mean relative difference of -0.3 ± 1.9% was not statistically significant. Low measurement variability, with a mean absolute percentage value of 0.6 ± 0.2%, was achieved. None of the PCF linear landmarks were significantly associated with PCF volume.
  
  
  CONCLUSIONS
  PCF and tissue content volumes can be reliably measured in patients with CMI by use of an atlas-based automated segmentation method.
authors.affiliation: Departments of Radiology.
authors.forename: A M
authors.initials: AM
authors.lastname: Bagci
authors.forename: S H
authors.initials: SH
authors.lastname: Lee
authors.forename: N
authors.initials: N
authors.lastname: Nagornaya
authors.forename: B A
authors.initials: BA
authors.lastname: Green
authors.forename: N
authors.initials: N
authors.lastname: Alperin
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001139:Arnold-Chiari Malformation / Q000473:pathology*; D001185:Artificial Intelligence; D003388:Cranial Fossa, Posterior / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult