Abstract
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.
Highlights
Preclinical in vivo imaging requires precise and reproducible delineation of brain structures
Dice indices were significantly different between methods (PF.maximum probability atlas (MP).single atlas (SA))
Positron emission tomography (PET) regional measures were more accurate with multi-atlas methods than with SA method
Summary
Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Analyzing functional images requires a corresponding anatomical image so as to identify brain structures. Without automated anatomical identification, analyzing PET data relies on time-consuming and observer-dependent manual volume-of-interest (VOI) delineation. Automated delineation usually consists in normalizing images in a reference space, and segmenting the structures in this space using a standardized digital brain atlas. This requires a normalization process, a template (i.e., an averaged image of individuals registered in the reference space, used as target in normalization) and a digital atlas
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