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

Authors:
Neil I. Weisenfeld;Andrea U. J. Mewes;Simon K. Warfield
Affiliations:
Computational Radiology Laboratory, Brigham and Women’s and Children’s Hospitals, Harvard Medical School, Boston, MA;Computational Radiology Laboratory, Brigham and Women’s and Children’s Hospitals, Harvard Medical School, Boston, MA;Computational Radiology Laboratory, Brigham and Women’s and Children’s Hospitals, Harvard Medical School, Boston, MA
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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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.