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 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.
authors.affiliation: Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA.
authors.forename: Jiahui
authors.initials: J
authors.lastname: Wang
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
authors.forename: Clement
authors.initials: C
authors.lastname: Vachet
authors.affiliation: Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA.
authors.forename: Ashley
authors.initials: A
authors.lastname: Rumple
authors.affiliation: Bioclinica Lyon, France.
authors.forename: Sylvain
authors.initials: S
authors.lastname: Gouttard
authors.affiliation: Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA.
authors.forename: Clémentine
authors.initials: C
authors.lastname: Ouziel
authors.affiliation: Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA.
authors.forename: Emilie
authors.initials: E
authors.lastname: Perrot
authors.affiliation: Department of Neurology, Neurosurgery and Radiology, Pennsylvania State University Milton Hershey Medical Center Hershey, PA, USA.
authors.forename: Guangwei
authors.initials: G
authors.lastname: Du
authors.affiliation: Department of Neurology, Neurosurgery and Radiology, Pennsylvania State University Milton Hershey Medical Center Hershey, PA, USA.
authors.forename: Xuemei
authors.initials: X
authors.lastname: Huang
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.affiliation: Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA ; Department of Computer Science, University of North Carolina Chapel Hill, NC, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Styner
keywords: Insight Toolkit; MRI; atlas; brain; registration; segmentation