Highly accurate segmentation of brain tissue and subcortical gray matter from newborn MRI

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 acquire...

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Bibliographic Details
Published in:	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 9; no. Pt 1; p. 199
Main Authors:	Weisenfeld, Neil I, Mewes, Andrea U J, Warfield, Simon K
Format:	Journal Article
Language:	English
Published:	Germany 2006
Subjects:	Algorithms Artificial Intelligence Brain - cytology Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Infant, Newborn Magnetic Resonance Imaging - methods Neurons - cytology Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity and Specificity
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Summary:	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.
DOI:	10.1007/11866565_25