Construction of a 3D probabilistic atlas of human cortical structures

Abstract

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

Fig. 1

Examples of probabilistic atlases. (a) An average intensity atlas computed from co-registered MRI data (b) A tissue-type probability atlas computed from co-registered tissue fraction maps produced from MRI. Each voxel was partitioned into grey matter, white matter, or CSF components in the original volumes (shown here in green, blue, or red, respectively). The values are transformed into the common atlas space, then used to compute the likelihood of each voxel in the atlas space containing the different tissue types. (c) A probabilistic structure atlas computed from co-registered cortical structure delineations. The delineations (see Table 1 for color index) are mapped into the atlas space, then used to create density maps for each structure in the volume. The figure shows a maximum likelihood labeling generated from a probabilistic structure atlas.

Fig. 2

Image pre-processing sequence. MRI of the brain for each subject were registered to the MNI-305 atlas (181 × 217 × 181 voxels, 1 × 1 × 1mm³ voxel resolution) using a rigid-body transformation. This corrected for head-tilt and alignment to ensure unbiased anatomical decisions during the delineation process. Once in the delineation space, the MRI were corrected for RF inhomogeneity artifacts and processed to identify the cerebrum.

Fig. 3

Delineation Example figures from the delineation protocol for the post–central gyrus. The left image shows the slice view, while the middle view shows the corresponding surface view. These views are consistent with what the raters see during the delineation process, except for the annotations, which are part of the written protocol. The rightmost figure shows the inclusion of white matter in the delineation of a gyrus. Text and images for all protocols used in this study are available from our website.

Fig. 4

These flowcharts show the common approach used in the production of the 3 atlas versions. Once the spatially normalized maps were produced for each subject, they were averaged to produce the average intensity and probability density maps. (a) Normalization of data from native space to the atlas space. The spatial normalization methods and target spaces used are detailed in Table 2. The skull-stripped, RF-corrected MRI were used for the AIR and FLIRT normalization processes; the whole-head MRI were used for the SPM5 normalization. The skull-stripped, RF-corrected MRI were resampled for all 3 versions of the atlas; see the text for details on the resampling parameters used. (b) Normalization of data from delineation space to the atlas space. The delineation labels were separated into individual structure maps and resampled using trilinear interpolation. For the AIR and FLIRT versions of the atlas, this was achieved using a single composited transform; for the SPM5 version, the structure maps were first resampled into the native space, then retransformed into the atlas space. Trilinear interpolation was used in each instance.

Fig. 5

Probability densities for the superior temporal gyrus, computed for each of the three atlas spaces. Each density is superimposed on the average intensity brain image computed for each target space. For the LPBA40/FLIRT and LPBA40/SPM5 images, the slices are taken through the z = 0 plane in SPM5 coordinates for the ICBM152 T1 average; the LPBA40/AIR images were taken from a corresponding plane. All images are shown in neurological convention (anatomical left is displayed on the left) (Top) The average intensity brain image (Middle) The superior temporal gyrus density map (Bottom) The superior temporal gyrus grey matter density map.

Fig. 6

Probability maps for three cortical structures. Probabilistic data from the LPBA40/AIR atlas was mapped back to the space of one of the atlas subjects. The surface model has been colored according to the probability values for middle frontal gyrus, superior temporal gyrus, and fusiform gyrus to indicate the likelihood of one of those structures at a given surface point.

Fig. 7

Average MRI intensity images. On the left are slices from the average brain images produced after spatial alignment with AIR nonlinear warping and the ICBM452 T1 5th order warp atlas. In the middle are similar slices in the average intensity image produced after alignment using FLIRT and its skull-stripped ICBM152 T1 average brain. On the right are the same slices in the images produced using SPM5 to align the subject data to its ICBM152 T1 template. Transaxial and coronal mages are displayed in neurological convention (anatomical left is displayed on the left); the crosshairs indicate the slice positions.

Fig. 8

Maximum likelihood maps. Shown are images from the maximum likelihood maps for the three versions of the atlas, corresponding to the slices shown in Fig. 7. The intensity represents that maximum probability value at each voxel, computed from the estimated structure probability density maps. The color indicates the most probable structure (see Table 1). Transaxial and coronal images are displayed in neurological convention (anatomical left is displayed on the left)).

Fig. 9

Maximum likelihood grey matter maps. Shown are images from the maximum likelihood grey matter maps for the three versions of the atlas, corresponding to the slices shown in Fig. 7. The intensity represents that maximum probability value at each voxel, computed from the estimated grey matter structure probability density maps. The color indicates the most probable structure (see Table 1). Transaxial and coronal images are displayed in neurological convention (anatomical left is displayed on the left).

Fig. 10

The LPBA40/AIR maximimum likelihood atlas data, mapped back to the surface model of an atlas subject. (Top) The surface has been colored according to the most likely structure label (see Table I for key). (Middle) The surface is colored according to the maximum probability value at each surface point. (Bottom) The surface has been colored according to the number of structures with non-zero probabilities at each location on the cortex.