title: Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters.
abstract: Volumetric measurements obtained from image parcellation have been instrumental in uncovering structure-function relationships. However, anatomical study of the cerebellum is a challenging task. Because of its complex structure, expert human raters have been necessary for reliable and accurate segmentation and parcellation. Such delineations are time-consuming and prohibitively expensive for large studies. Therefore, we present a three-part cerebellar parcellation system that utilizes multiple inexpert human raters that can efficiently and expediently produce results nearly on par with those of experts. This system includes a hierarchical delineation protocol, a rapid verification and evaluation process, and statistical fusion of the inexpert rater parcellations. The quality of the raters' and fused parcellations was established by examining their Dice similarity coefficient, region of interest (ROI) volumes, and the intraclass correlation coefficient of region volume. The intra-rater ICC was found to be 0.93 at the finest level of parcellation.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. bogovic@jhu.edu
authors.forename: John A
authors.initials: JA
authors.lastname: Bogovic
authors.forename: Bruno
authors.initials: B
authors.lastname: Jedynak
authors.forename: Rachel
authors.initials: R
authors.lastname: Rigg
authors.forename: Annie
authors.initials: A
authors.lastname: Du
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
authors.forename: Sarah H
authors.initials: SH
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D001284:Atrophy / Q000473:pathology; D002531:Cerebellum / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011361:Professional Competence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity