title: Automated MRI cerebellar size measurements using active appearance modeling.
abstract: Although the human cerebellum has been increasingly identified as an important hub that shows potential for helping in the diagnosis of a large spectrum of disorders, such as alcoholism, autism, and fetal alcohol spectrum disorder, the high costs associated with manual segmentation, and low availability of reliable automated cerebellar segmentation tools, has resulted in a limited focus on cerebellar measurement in human neuroimaging studies. We present here the CATK (Cerebellar Analysis Toolkit), which is based on the Bayesian framework implemented in FMRIB's FIRST. This approach involves training Active Appearance Models (AAMs) using hand-delineated examples. CATK can currently delineate the cerebellar hemispheres and three vermal groups (lobules I-V, VI-VII, and VIII-X). Linear registration with the low-resolution MNI152 template is used to provide initial alignment, and Point Distribution Models (PDM) are parameterized using stellar sampling. The Bayesian approach models the relationship between shape and texture through computation of conditionals in the training set. Our method varies from the FIRST framework in that initial fitting is driven by 1D intensity profile matching, and the conditional likelihood function is subsequently used to refine fitting. The method was developed using T1-weighted images from 63 subjects that were imaged and manually labeled: 43 subjects were scanned once and were used for training models, and 20 subjects were imaged twice (with manual labeling applied to both runs) and used to assess reliability and validity. Intraclass correlation analysis shows that CATK is highly reliable (average test-retest ICCs of 0.96), and offers excellent agreement with the gold standard (average validity ICC of 0.87 against manual labels). Comparisons against an alternative atlas-based approach, SUIT (Spatially Unbiased Infratentorial Template), that registers images with a high-resolution template of the cerebellum, show that our AAM approach offers superior reliability and validity. Extensions of CATK to cerebellar hemisphere parcels are envisioned.
authors.affiliation: Neurobehavioral Research, Inc., Ala Moana Pacific Center, 1585 Kapiolani Blvd. Suite 1030, Honolulu, HI 96814, USA.
authors.forename: Mathew
authors.initials: M
authors.lastname: Price
authors.affiliation: Neurobehavioral Research, Inc., Ala Moana Pacific Center, 1585 Kapiolani Blvd. Suite 1030, Honolulu, HI 96814, USA.
authors.forename: Valerie A
authors.initials: VA
authors.lastname: Cardenas
authors.affiliation: Neurobehavioral Research, Inc., Ala Moana Pacific Center, 1585 Kapiolani Blvd. Suite 1030, Honolulu, HI 96814, USA. Electronic address: george@nbresearch.com.
authors.forename: George
authors.initials: G
authors.lastname: Fein
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002531:Cerebellum / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009929:Organ Size; D015203:Reproducibility of Results
keywords: Active Appearance Models; Automated Analysis; Cerebellum; MRI; Segmentation