Paper Info
Title
Fully automated analysis using BRAINS: AutoWorkup.
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.
Year
DOI
Venue
2011
10.1016/j.neuroimage.2010.06.047
NeuroImage
Keywords
Field
DocType
BRAINS,Automated image analysis,Pipeline,Volumetric analysis,Morphometry,Segmentation
Computer vision,Normalization (statistics),Data analysis,Computer science,Segmentation,Image analysis,Automation,Artificial intelligence,Jaccard index,Scanner,Digital image processing
Journal
Volume
Issue
ISSN
54
1
1053-8119
Citations 
PageRank 
References 
10
0.90
8
Authors
7
Name
Order
Citations
PageRank
Ronald Pierson1895.92
Hans Johnson2100.90
Gregory Harris31621.11
Helen Keefe4101.24
Jane Paulsen5375.47
Nancy Andreasen612016.35
Vincent A Magnotta76914.88