Journal article
Fully automated analysis using BRAINS: AutoWorkup
NeuroImage (Orlando, Fla.), Vol.54(1), pp.328-336
01/01/2011
DOI: 10.1016/j.neuroimage.2010.06.047
PMCID: PMC3827877
PMID: 20600977
Abstract
The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC≥0.96, Jaccard index≥0.80, and Dice index ≥0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.
►BRAINS AutoWorkup pipeline for fast analysis of structural MRI ►Reliable, accurate automated structural MRI analysis ►Combine cross-scanner, cross-sequence MRI data ►Reliable multisite MRI structural imaging analysis.
Details
- Title: Subtitle
- Fully automated analysis using BRAINS: AutoWorkup
- Creators
- Ronald Pierson - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USAHans Johnson - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USAGregory Harris - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USAHelen Keefe - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USAJane S Paulsen - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USANancy C Andreasen - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USAVincent A Magnotta - The University of Iowa Roy and Lucille Carver College of Medicine, Department of Radiology, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- NeuroImage (Orlando, Fla.), Vol.54(1), pp.328-336
- DOI
- 10.1016/j.neuroimage.2010.06.047
- PMID
- 20600977
- PMCID
- PMC3827877
- NLM abbreviation
- Neuroimage
- ISSN
- 1053-8119
- eISSN
- 1095-9572
- Publisher
- Elsevier Inc
- Grant note
- DOI: 10.13039/100000065, name: National Institute of Neurological Disorders and Stroke, award: NS050568, NS40068; DOI: 10.13039/100000025, name: National Institute of Mental Health, award: MH31593, MH40856; name: MHCRC, award: MHCRC43271; DOI: 10.13039/100005725, name: CHDI Foundation, Inc
- Language
- English
- Date published
- 01/01/2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984003474402771
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