A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity
Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, e...
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| Published in: | PloS one Vol. 9; no. 10; p. e109113 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Public Library of Science
17-10-2014
Public Library of Science (PLoS) |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.
High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).
Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.
Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 PMCID: PMC4201469 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: SL NC. Performed the experiments: SL RR AS CB EL J-BL NC. Analyzed the data: SL RR NC. Contributed reagents/materials/analysis tools: SL RR AS CB EL NC. Contributed to the writing of the manuscript: SL LZ NC. |
| ISSN: | 1932-6203 1932-6203 |
| DOI: | 10.1371/journal.pone.0109113 |