Paper Info
Title
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline.
Abstract
Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing Batch Make for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures.
Year
DOI
Venue
2014
10.3389/fninf.2014.00007
FRONTIERS IN NEUROINFORMATICS
Keywords
Field
DocType
segmentation,registration,MRI,atlas,brain,Insight Toolkit
Data mining,Population,Scale-space segmentation,Segmentation,Computer science,Directed graph,Segmentation-based object categorization,Image segmentation,Software,Cluster analysis
Journal
Volume
ISSN
Citations 
8
1662-5196
15
PageRank 
References 
Authors
1.07
29
10
Name
Order
Citations
PageRank
Jiahui Wang1365.52
Clement Vachet2415.34
Ashley Rumple3201.90
Sylvain Gouttard431418.81
Clémentine Ouziel5151.07
Emilie Perrot6151.07
Guangwei Du7151.07
Xuemei Huang8151.07
Guido Gerig94795540.21
Martin Styner10432.83