Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline
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 we...
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doaj-66fec38514754162a2ae5182d73f38c52020-11-24T23:57:33ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962014-02-01810.3389/fninf.2014.0000773870Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipelineJiahui eWang0Clement eVachet1Ashley eRumple2Sylvain eGouttard3Clementine eOuziel4Emilie ePerrot5Guangwei eDu6Xuemei eHuang7Guido eGerig8Martin Andreas Styner9Martin Andreas Styner10The University of North CarolinaUniversity of UtahThe University of North CarolinaBioclinicaNagravisionCabinet Beau de LomeniePennsylvania State UnivesityPennsylvania State UnivesityUniversity of UtahThe University of North CarolinaUniversity of North Carolina at Chapel HillAutomated 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.http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00007/fullBrainMRIAtlasRegistrationsegmentationInsight Toolkit |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiahui eWang Clement eVachet Ashley eRumple Sylvain eGouttard Clementine eOuziel Emilie ePerrot Guangwei eDu Xuemei eHuang Guido eGerig Martin Andreas Styner Martin Andreas Styner |
spellingShingle |
Jiahui eWang Clement eVachet Ashley eRumple Sylvain eGouttard Clementine eOuziel Emilie ePerrot Guangwei eDu Xuemei eHuang Guido eGerig Martin Andreas Styner Martin Andreas Styner Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline Frontiers in Neuroinformatics Brain MRI Atlas Registration segmentation Insight Toolkit |
author_facet |
Jiahui eWang Clement eVachet Ashley eRumple Sylvain eGouttard Clementine eOuziel Emilie ePerrot Guangwei eDu Xuemei eHuang Guido eGerig Martin Andreas Styner Martin Andreas Styner |
author_sort |
Jiahui eWang |
title |
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline |
title_short |
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline |
title_full |
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline |
title_fullStr |
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline |
title_full_unstemmed |
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline |
title_sort |
multi-atlas segmentation of subcortical brain structures via the autoseg software pipeline |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2014-02-01 |
description |
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. |
topic |
Brain MRI Atlas Registration segmentation Insight Toolkit |
url |
http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00007/full |
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