Research article

Mindboggle: Automated brain labeling with multiple atlases

Arno Klein^1,2 , Brett Mensh³ , Satrajit Ghosh⁴ , Jason Tourville⁵ and Joy Hirsch¹

¹fMRI Research Center, Columbia University, New York, USA

²Parsons Institute for Information Mapping, The New School, New York, USA

³New York State Psychiatric Institute, Columbia University, New York, USA

⁴Speech Communication Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, USA

⁵Department of Cognitive and Neural Systems, Boston University, Boston, USA

author email corresponding author email

BMC Medical Imaging 2005, 5:7doi:10.1186/1471-2342-5-7

Published:	5 October 2005

Abstract

Background

To 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.

Methods

Each 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.

Results

When 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.

Conclusion

Increasing 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.