Abstract
BackgroundTo make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data.MethodsEach brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently.ResultsWhen Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions.ConclusionIncreasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images.
Highlights
To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data
When comparing structures or functions across brains, it is common to label the gross anatomy of brain image data and to compare the structures or functions that lie within anatomically labeled regions
Since brains differ in their anatomy [1,2,3,4,5,6,7,8,9,10], it would seem reasonable to refer to the
Summary
Image acquisition We used two sets of T1-weighted MRI data from a total of 20 young, healthy adult subjects. The manual labels used for evaluation were used to construct Mindboggle atlases They were assigned by a single human labeler to each of the 20 subject brains (before linear registration to the MNI152 space), according to one of two different parcellation schemes. The agreement between atlas label set Ai and manual label set Mi is defined as the volume of intersection divided by the volume of the manually labeled region, computed in voxels and summed over a set of multiple labeled regions each with index i, where |.| indicates number of voxels: QFiugaunrteity5of voxels with a given number of labels per voxel Quantity of voxels with a given number of labels per voxel This is a graph of all the subject data labeled with an increasing number of atlases, from which the single subject in Figure 2 was drawn. Mindboggle is sensitive to variance in the subject population, and to the parcellation scheme used to manually label the atlases
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
