TY - GEN
T1 - Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain
AU - Schwarz, Christopher G.
AU - Tsui, Alex
AU - Fletcher, Evan
AU - Singh, Baljeet
AU - Decarli, Charles
AU - Carmichael, Owen
PY - 2011
Y1 - 2011
N2 - Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly data sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Belief propagation (BP) and graph cuts (GC), used as the MRF optimizer component of a standardized segmentation system, provide high segmentation performance on aggregate that is competitive with end-to-end systems provided by SPM and FSL (FAST) as well as the more traditional MRF optimizer iterated conditional modes (ICM). However, the relative performance of each method varied strongly by performance criterion and differed between young and old brains. The findings emphasize the unique difficulties involved in segmenting the aging brain, and suggest that optimal algorithm components may depend in part on performance criteria.
AB - Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly data sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Belief propagation (BP) and graph cuts (GC), used as the MRF optimizer component of a standardized segmentation system, provide high segmentation performance on aggregate that is competitive with end-to-end systems provided by SPM and FSL (FAST) as well as the more traditional MRF optimizer iterated conditional modes (ICM). However, the relative performance of each method varied strongly by performance criterion and differed between young and old brains. The findings emphasize the unique difficulties involved in segmenting the aging brain, and suggest that optimal algorithm components may depend in part on performance criteria.
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U2 - 10.1109/IEMBS.2011.6091925
DO - 10.1109/IEMBS.2011.6091925
M3 - Conference contribution
C2 - 22256150
AN - SCOPUS:84861665981
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7812
EP - 7815
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -