Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain

Christopher G. Schwarz, Alex Tsui, Evan Fletcher, Baljeet Singh, Charles Decarli, Owen Carmichael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages7812-7815
Number of pages4
DOIs
StatePublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Schwarz, C. G., Tsui, A., Fletcher, E., Singh, B., Decarli, C., & Carmichael, O. (2011). Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 7812-7815). [6091925] https://doi.org/10.1109/IEMBS.2011.6091925