Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images

Kristi A. Clark, Roger P. Woods, David A. Rottenberg, Arthur W. Toga, John C. Mazziotta

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


The segmentation of T1-weighted images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is a fundamental processing step in neuroimaging, the results of which affect many other structural imaging analyses. Variability in the segmentation process can decrease the power of a study to detect anatomical differences, and minimizing such variability can lead to more robust results. This paper outlines a straightforward strategy that can be used (1) to select more optimal data acquisition and processing protocols and (2) to quantify the impact of such optimization. Using this approach with multiple scans of a single subject, we found that the choice of a segmentation algorithm had the largest impact on variability, while the choice of a pulse sequence had the second largest impact. The data indicate that the classification of GM is the most variable, and that the optimal protocol may differ across tissue types. Therefore, the intended use of segmentation data should play a role in optimization. Examples are provided to demonstrate that the minimization of variability is not sufficient for optimization; the overall accuracy of the approach must also be considered. Simple volumetric computations are included to illustrate the potential gain of optimization; these results show that volume estimates from optimal pathways were on average three times less variable than estimates from suboptimal pathways. Therefore, the simple strategy illustrated here can be applied to many studies to optimize tissue segmentation, which should lead to a net increase in the power of structural neuroimaging studies.

Original languageEnglish (US)
Pages (from-to)185-202
Number of pages18
Issue number1
StatePublished - Jan 1 2006

Bibliographical note

Funding Information:
Support for this work was provided by a grant from the Human Brain Project (P20-MHDA52176), the National Institute of Biomedical Imaging and Bioengineering, National Institute of Mental Health, National Institute for Drug Abuse, National Cancer Institute, and the National Institute for Neurologic Disease and Stroke. This work was supported by a training grant #R01 MH071940 from the National Institute of Mental Health and the National Institute of Neurological Disorders and Stroke. Additional support was provided by a resource grant from the National Center for Research Resources (P41 RR013642). Additional support was provided by a grant from the National Institute of Health (P20-EB02013). For generous support, the authors also wish to thank the Brain Mapping Medical Research Organization, Brain Mapping Support Foundation, Pierson-Lovelace Foundation, The Ahmanson Foundation, Tamkin Foundation, Jennifer Jones-Simon Foundation, Capital Group Companies Charitable Foundation, Robson Family, William M. and Linda R. Dietel Philanthropic Fund at the Northern Piedmont Community Foundation, Northstar Fund, and the National Center for Research Resources grants RR12169, RR13642 and RR08655.


  • MRI
  • Reliability
  • Segmentation
  • Structural neuroimaging
  • Tissue classification
  • Validation


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