title: Highly accurate segmentation of brain tissue and subcortical gray matter from newborn MRI.
abstract: The segmentation of newborn brain MRI is important for assessing and directing treatment options for premature infants at risk for developmental disorders, abnormalities, or even death. Segmentation of infant brain MRI is particularly challenging when compared with the segmentation of images acquired from older children and adults. We sought to develop a fully automated segmentation strategy and present here a Bayesian approach utilizing an atlas of priors derived from previous segmentations and a new scheme for automatically selecting and iteratively refining classifier training data using the STAPLE algorithm. Results have been validated by comparison to hand-drawn segmentations.
authors.affiliation: Computational Radiology Laboratory, Brigham and Women's and Children's Hospitals, Harvard Medical School, Boston, MA.
authors.forename: Neil I
authors.initials: NI
authors.lastname: Weisenfeld
authors.forename: Andrea U J
authors.initials: AU
authors.lastname: Mewes
authors.forename: Simon K
authors.initials: SK
authors.lastname: Warfield
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity