- Email: [email protected]
Automatic labeling of brain anatomy and FMRI brain activity
NemoImage
13, Number
6, 2001, Part 2 of 2 Parts
lDEklW
METHODS
- ANALYSIS
Automatic labeling of brain anatomy and FMRI brain activity Arno Klein, Joy Hirsch FMRI Laboratory of the Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, USA Almost any comparative structural or functional brain study necessitates the identification of corresponding anatomical structures. Manually ascribing anatomical labels to individual brain image data is tedious, difficult, and very inconsistent. The motivation to automatize the process of labeling anatomical (and therefore functional) image data is therefore to provide a consistent and convenient method for preparation of data for analysis. This abstract briefly describes a new, fully automatic approach to labeling the anatomy and functional activity of a subject’s brain. Methods Co-registration
of brain
atlas
and subject
MRI
volume
The MRI (Tl) volumes of the Harvard brain atlas [l] and a subject are co-registered with the Montreal Neurological Institute’s average of 305 MRI volumes set in Talairach space [2],[3]. MNI’s auto-registration package is also used to register the functional (T2*) with the anatomical (Tl) MRI images. Image
pre-processing
Morphological points from pre-processed Self-organized
operations are performed to extract skeletons the skeletons. The composite 3-D skeletons in the same manner. mapping
of brain
mappings
with
neural network in 3-D.
the subject
and branch selected is
[4], also called
a self-organizing
map, is used to map combinations
a cost function
A cost function is used to determine which set of atlas-subject function penalizes mapping distortion, resulting dissimilarity, atlas and subject regions. Labeling
extract endpoints The brain atlas
regions
A modified form of an unsupervised Kohonen of atlas brain regions to subject brain regions Evaluating
of the (Tl) brain sections. 2-D filters are segmented into smaller regions.
mappings makes for a reasonable and differences in normalized
piece-to-piece correspondence. position, size, and branching
The of the
brain
The labels for the atlas regions color-labeled) activity data.
matched
to each subject
region
are used to label the subject
region
as well as any co-registered
(T2*
Conclusion In this study, anatomical labels are automatically assigned to brain image data with the use of a labeled brain atlas. The automatic labels are evaluated by comparison with labels assigned by experts, and with functional data of known regions acquired with standard tasks. Results are given as measures of correspondence between these independent labeling methods, in percent shared voxel labels. References Kikinis, et al (1996). A digital brain atlas for surgical planning, model driven segmentation and teaching. IEEE Transactions on Visualization and Computer Graphics, 2. Evans, A.C., et al (1994). Three-dimensional correlative imaging: applications in human brain mapping, in: Functional Neuroimaging: Technical Foundations (Thatcher, R.W., et al, eds), 145162. Collins, D.L., et al (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Jountal of Computer Assisted Tomography, 18, 192-205. Kohonen, T. (1997). Self-organizing maps, 2nd Ed. New York: Springer-Verlag.
s174
13, Number
6, 2001, Part 2 of 2 Parts
lDEklW
METHODS
- ANALYSIS
Automatic labeling of brain anatomy and FMRI brain activity Arno Klein, Joy Hirsch FMRI Laboratory of the Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, USA Almost any comparative structural or functional brain study necessitates the identification of corresponding anatomical structures. Manually ascribing anatomical labels to individual brain image data is tedious, difficult, and very inconsistent. The motivation to automatize the process of labeling anatomical (and therefore functional) image data is therefore to provide a consistent and convenient method for preparation of data for analysis. This abstract briefly describes a new, fully automatic approach to labeling the anatomy and functional activity of a subject’s brain. Methods Co-registration
of brain
atlas
and subject
MRI
volume
The MRI (Tl) volumes of the Harvard brain atlas [l] and a subject are co-registered with the Montreal Neurological Institute’s average of 305 MRI volumes set in Talairach space [2],[3]. MNI’s auto-registration package is also used to register the functional (T2*) with the anatomical (Tl) MRI images. Image
pre-processing
Morphological points from pre-processed Self-organized
operations are performed to extract skeletons the skeletons. The composite 3-D skeletons in the same manner. mapping
of brain
mappings
with
neural network in 3-D.
the subject
and branch selected is
[4], also called
a self-organizing
map, is used to map combinations
a cost function
A cost function is used to determine which set of atlas-subject function penalizes mapping distortion, resulting dissimilarity, atlas and subject regions. Labeling
extract endpoints The brain atlas
regions
A modified form of an unsupervised Kohonen of atlas brain regions to subject brain regions Evaluating
of the (Tl) brain sections. 2-D filters are segmented into smaller regions.
mappings makes for a reasonable and differences in normalized
piece-to-piece correspondence. position, size, and branching
The of the
brain
The labels for the atlas regions color-labeled) activity data.
matched
to each subject
region
are used to label the subject
region
as well as any co-registered
(T2*
Conclusion In this study, anatomical labels are automatically assigned to brain image data with the use of a labeled brain atlas. The automatic labels are evaluated by comparison with labels assigned by experts, and with functional data of known regions acquired with standard tasks. Results are given as measures of correspondence between these independent labeling methods, in percent shared voxel labels. References Kikinis, et al (1996). A digital brain atlas for surgical planning, model driven segmentation and teaching. IEEE Transactions on Visualization and Computer Graphics, 2. Evans, A.C., et al (1994). Three-dimensional correlative imaging: applications in human brain mapping, in: Functional Neuroimaging: Technical Foundations (Thatcher, R.W., et al, eds), 145162. Collins, D.L., et al (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Jountal of Computer Assisted Tomography, 18, 192-205. Kohonen, T. (1997). Self-organizing maps, 2nd Ed. New York: Springer-Verlag.
s174