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Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging
Author(s) -
Kikinis Ron,
Shenton Martha E.,
Gerig Guido,
Martin John,
Anderson Mark,
Metcalf David,
Guttmann Charles R. G.,
McCarley Robert W.,
Jolesz Ferenc A.,
Lorensen William,
Cline Harvey
Publication year - 1992
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.1880020603
Subject(s) - segmentation , magnetic resonance imaging , computer science , white matter , reliability (semiconductor) , homogeneity (statistics) , neuroradiologist , nuclear medicine , automated method , artificial intelligence , pattern recognition (psychology) , radiology , medicine , machine learning , power (physics) , physics , quantum mechanics
A computerized system for processing spin‐echo magnetic resonance (MR) imaging data was implemented to estimate whole brain (gray and white matter) and cerebrospinal fluid volumes and to display three‐dimensional surface reconstructions of specified tissue classes. The techniques were evaluated by assessing the radiometric variability of MR volume data and by comparing automated and manual procedures for measuring tissue volumes. Results showed (a) the homogeneity of the MR data and (b) that automated techniques were consistently superior to manual techniques. Both techniques, however, were affected by the complexity of the structure, with simpler structures (eg, the intracranial cavity) showing less variability and better spatial correlation of segmentation results between raters. Moreover, the automated techniques were completed for whole brain in a fraction of the time required to complete the equivalent segmentation manually. Additional evaluations included interrater reliability and an evaluation that included longitudinal measurement, in which one subject was imaged sequentially 24 times, with reliability computed from data collected by three raters over 1 year. Results showed good reliability for the automated segmentation procedures.