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Multi-atlas Segmentation of Subcortical Brain Structures via the AutoSeg Software Pipeline

1Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
2Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
3Bioclinica, Lyon, France.
4Department of Neurology, Neurosurgery and Radiology, Pennsylvania State University Milton Hershey Medical Center, Hershey, PA, USA.
Publication Date:
Volume Number:
Front Neuroinform. 2014 Feb; 8: 7.
PubMed ID:
Segmentation, Registration, MRI, Atlas, Brain, Insight Toolkit
Appears in Collections:
R41 NS059095/NS/NINDS NIH HHS/United States
U24 NS059696/NS/NINDS NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 NS060722/NS/NINDS NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
P50 AG005681/AG/NIA NIH HHS/United States
P01 AG003991/AG/NIA NIH HHS/United States
R01 AG021910/AG/NIA NIH HHS/United States
P50 MH071616/MH/NIMH NIH HHS/United States
U24 RR021382/RR/NCRR NIH HHS/United States
R01 MH056584/MH/NIMH NIH HHS/United States
Generated Citation:
Wang J., Vachet C., Rumple A., Gouttard S., Ouziel C., Perrot E., Du G., Huang X., Gerig G., Styner M. Multi-atlas Segmentation of Subcortical Brain Structures via the AutoSeg Software Pipeline. Front Neuroinform. 2014 Feb; 8: 7. PMID: 24567717. PMCID: PMC3915103.
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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 BatchMake 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.

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JWang-NeuromusculDisord2014-fig5.jpg (180.881kB)