A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity

Autor: Elise Levigoureux, Caroline Bouillot, Sophie Lancelot, Afifa Slimen, Luc Zimmer, Roxane Roche, Nicolas Costes, Jean-Baptiste Langlois
Přispěvatelé: Radiopharmaceutical and Neurochemical Biomarkers Team (BioRaN), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)
Rok vydání: 2014
Předmět:
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging
Computer science
Nervous System
Brain mapping
030218 nuclear medicine & medical imaging
Rats
Sprague-Dawley
0302 clinical medicine
Image Processing
Computer-Assisted
Brain segmentation
Mammals
Brain Mapping
Multidisciplinary
medicine.diagnostic_test
Atlas (topology)
Brain
Magnetic Resonance Imaging
In Vivo Imaging
medicine.anatomical_structure
Positron emission tomography
Vertebrates
Medicine
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Anatomy
Preclinical imaging
Research Article
Imaging Techniques
Science
education
Neuroimaging
Image processing
Image Analysis
Research and Analysis Methods
Rodents
Sensitivity and Specificity
03 medical and health sciences
Atlases as Topic
Atlas (anatomy)
Image Interpretation
Computer-Assisted
medicine
Animals
Biology and life sciences
business.industry
Neurotransmission
Organisms
Sprague-Dawley rats
Magnetic resonance imaging
Pattern recognition
Rats
Neuroanatomy
Positron-Emission Tomography
Animal Studies
Artificial intelligence
Nuclear medicine
business
030217 neurology & neurosurgery
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Public Library of Science, 2014, 9 (10), pp.0109113. ⟨10.1371/journal.pone.0109113⟩
PLoS ONE, Vol 9, Iss 10, p e109113 (2014)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0109113
Popis: IntroductionPreclinical 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.MethodsHigh-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).ResultsOnly 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.ConclusionsMulti-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.
Databáze: OpenAIRE
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