Construction of a 3D probabilistic atlas of human cortical structures
Introduction
Atlases of human neuroanatomy play important roles in the interpretation of results, in the visualization of information, and in the processing of data. Beginning with the detailed drawings of brain structures produced during the Renaissance by Vesalius, numerous paper atlases comprising collections of neuroanatomical illustrations, photographs, and other imagery have been constructed (see Toga and Mazziotta, 2002 for a review). As technology advanced, digital atlases extended these efforts by providing interactive collections of brain data. Most of these brain atlases were based on single subjects or on very limited numbers of individuals. These included the Voxel–Man atlas (Höhne et al., 1992, Tiede et al., 1993, Schiemann et al., 2000), the Karolinska Brain Atlas project (Greitz et al., 1991), the Human Brain Atlas (Roland et al., 1994), the Digital Anatomist project (Eno et al., 1991, Brinkley et al., 1997), the Harvard Brain Atlas (Kikinis et al., 1996), and the Visible Human Project (Spitzer et al., 1996). The focus on individual subjects allowed considerable effort to be spent detailing the neuroanatomy or acquiring multiple types of data from the subjects. However, single subject atlases cannot describe the variability in brain structure that is inherent across the human population. To capture this information, larger numbers of subjects must be examined.
One of the early descriptions of a multisubject atlas was presented by Mazziotta et al. (1995), who proposed the development of a comprehensive probabilistic brain atlas under the banner of the International Consortium for Brain Mapping (ICBM). This project has collected data from 5,300 subjects, including images of the brain using various magnetic resonance imaging (MRI) modalities, genetic material, and demographic information (Mazziotta et al., 2001). Multi-subject studies such as the ICBM project require methods that can bring the image data from different subjects into a common coordinate frame; numerous research efforts have been made to meet these demands (e.g., Bajcsy et al., 1983, Gee et al., 1993, Woods et al., 1998, Woods et al., 1993 Collins et al., 1994, Davatzikos et al., 1996, Wells et al., 1996, Thompson and Toga, 1996, Christensen et al., 1997, Ashburner and Friston, 1997, Ashburner et al., 1999, Fischl et al., 1999, Jenkinson and Smith, 2001, Jenkinson et al., 2002, Johnson and Christensen, 2002, Shen and Davatzikos, 2002, Xue et al., 2006). These techniques extended the method introduced by Talairach and his colleagues (Talairach et al., 1967, Talairach and Tournoux, 1988) for mathematically mapping an individual subject brain to an atlas.
Evans et al. (1993) applied registration methods (Collins et al., 1994) to produce a probabilistic map of MRI data acquired from 305 subjects. The MRI volumes were co-registered using a nine-parameter linear transformation and then averaged at each voxel to produce an average intensity atlas, termed MNI-305. A second atlas was produced by registering a subset of 152 brains to the MNI-305 atlas, again using a 9-parameter linear transformation, to generate the ICBM152 atlas. These templates provide a registration target for various analysis tools, such as SPM (Ashburner et al., 1999) and FSL (Smith et al., 2004), both of which use versions of the ICBM152 atlas as an anatomical reference. ICBM also produced an average intensity atlas from 452 subjects using affine registration, and a second atlas was produced using 5th order polynomial warps that improved cortical definition (Rex et al., 2003). Though affine alignment can bring many subcortical structures into alignment, regions of the brain vary substantially across subjects. Because of this, structures such as cortex are often blurry in average intensity volumetric atlases. This may occur even when non-linear registration methods are applied, as seen in Fig. 1a. Thus, it may be difficult to interpret location within an intensity-averaged atlas.
One way to address this problem is to create a multi-subject label atlas, in which voxel identifiers from the original subjects are transformed into the atlas space. Statistics of these transformed labels can then be computed at each voxel location to provide probabilistic information about the structures in that atlas. Examples include maps of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) (see Fig. 1b) or individual brain structures such as gyri or nuclei (see Fig. 1c). Probabilistic atlases can also be generated based on deformation maps (e.g., Thompson et al., 2004, Xue et al., 2006). Deformation–map methods analyze the transformations required to bring the subjects into alignment in order to analyze the variation of structures. Measures computed on the deformation processes can then be used to describe the variation seen in the volume or surface models for the studied subjects.
Due to the tremendous burden of manual delineation, as well as concerns of rater reliability, Collins et al. (1999) applied automated labeling techniques to produce a probabilistic mapping of structures within the subject data composing the ICBM152, thus creating a multi-subject structural atlas. The ICBM project has also produced a series of probabilistic volumes defining the voxel-wise frequency of several structures based upon manual delineations, which included maps of the lobes and of subcortical structures that were manually labeled in the volumetric data and sulcal maps that were generated by sampling curves that were manually traced on cortical surface models.1 Hammers et al. (2003) produced a maximum-probability atlas of the human brain based on a set of 20 manually delineated image volumes. The production of this atlas emphasized labeling structures in the temporal lobe. The data were aligned to the ICBM152 atlas using SPM99 (Ashburner et al., 1999) to produce an atlas that provides a basis for analysis of functional imaging of temporal lobe epilepsy. The collection of labels was later extended by incorporating additional structures and subjects; the augmented collection was used for automated labeling of brain structures (Heckemann et al., 2006). Van Essen (2005) produced a publicly available Population-Average, Landmark- and Surface-based (PALS) atlas from structural volumes of 12 individuals; the atlas can be used for surface-based analysis such as analysis of cortical folding abnormalities (Van Essen et al., 2006). Mega et al. (2005) examined variability in 20 elderly subjects with various mental states (normal cognition, mild cognitive impairment, or Alzheimer’s disease) by constructing an atlas from 68 manually delineated subregions. In that study, the authors also compared the use of 3 registration approaches and concluded that different methods may be appropriate for different areas of the brain. Klein et al. (2005) produced an automated method based on a set of 20 manually labeled brains, each with 36 labels per hemisphere. In their work, each labeled brain was registered to the subject brain and served as an atlas. The most frequently occurring label at each voxel was then selected to label that voxel in the subject brain. Atlasing is used in numerous other anatomical studies, though it is frequently used as a tool for analyzing the data without the goal of producing a widely distributed reference data set. Atlasing methods have also been applied in multi-subject studies of individual substructures of the brain, such as the hippocampus (Csernansky et al., 1998, Styner et al., 2004).
In this paper, we describe the construction of a probabilistic atlas of human brain structures. We produced our atlas from manual delineations of high resolution T1-weighted MRI scans of 40 healthy volunteers. A total of 50 cortical structures, 4 subcortical areas, the brainstem, and the cerebellum were delineated by trained raters following protocols developed during the course of this study. These data were then resampled into common spaces in order to produce estimates of the probability density functions for each structure. We produced 3 versions of our atlas using 3 widely used spatial normalization techniques paired with associated atlases. Each version of the atlas includes probabilistic maps of these structures, probabilistic maps of tissue types, and a volumetric average of the intensity data.
Among its uses, the atlas can serve as a reference template for studies of functional data or neuroanatomy, or as a statistical prior for segmentation algorithms. The delineated data can also provide a basis for the analysis of automated image segmentation methods or as training data for machine learning algorithms. The three versions of the atlas and the protocols used to perform the delineations are all available online.
Section snippets
Subjects and data acquisition
Forty volunteers were scanned with MRI at the North Shore–Long Island Jewish Health System (NSLIHS). Inclusion criteria for healthy volunteers included ages 16 to 40 and denial of any history of psychiatric or medical illness as determined by clinical interview. Exclusion criteria for all study participants included serious neurological or endocrine disorder, any medical condition or treatment known to affect the brain, or meeting DSM-IV criteria for mental retardation. The volunteer group was
Delineation
The delineation procedures were performed on all 40 subject volumes according to the delineation protocols. The protocol training and reliability testing required between 24 and 40 h per structure for each rater. The time required for delineation by each rater was approximately 1 h per structure per hemisphere. For the entire set of 40 volumes, each structure required on the order of 120 h of total rater time including training and delineation. Approximately 10 h of total rater time per volume
Discussion
In this paper, we have described the production of a probabilistic atlas of human brain structures. The atlas was generated from a set of T1-weighted MRI volumes collected from 40 healthy volunteers. Each MRI volume was delineated by trained raters to identify 56 brain structures. The raters followed carefully written protocols and were tested for interrater reliability on a subset of the data. The atlas data sets produced by this research are being made available publicly via our website, //www.loni.ucla.edu/Atlases/LPBA40
Acknowledgments
This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54-RR021813 (PI: Toga). Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics. Funding for this project was also provided by P41-RR013642 (PI: Toga), by NIH Roadmap P20-RR020750 (PI: Bilder) and by R01-MH60374 (PI: Bilder).
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