title: A feature-based developmental model of the infant brain in structural MRI.
abstract: In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.
authors.affiliation: Brigham and Women's Hospital, Harvard Medical School, USA. mt@bwh.harvard.edu
authors.forename: Matthew
authors.initials: M
authors.lastname: Toews
authors.forename: William M
authors.initials: WM
authors.lastname: Wells
authors.forename: Lilla
authors.initials: L
authors.lastname: Zöllei
mesh_terms: D000375:Aging / Q000473:pathology* / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity