. Human Brain Mapping5:238-242(1997)c

Automated Labelingof the Human Brain:
A Preliminary Report on the Development
and Evaluation of a Forward-Transform Method
J.L. Lancaster,r* L.H. Rainey,'J.L. Summerlin,r C.S. Freitas,r P.T. Fox,lA.C. Evans,2 A.W. Toga,3 and J.C. M.azziotta3lResenrch
ImagingCenter,Uniuersityof TexasHeahhScience
Centerat SanAntonio,
SanAntonio,kxns 78284-6240
2MontrealNeurologicnl
Unit, Magill Uniaersity,Montreol,Quebec,
CnnadaH3A2B4
3Laboratoryof Neurologicallmaging,Uniaersityof Californiant LosAngeles,LosAngeles,Cnlifornia90024-17
69aaAbstract: A forward-transform method for retrieving brain labels from the 1988 Talairach Atlas using x-y-z
coordinates is presented. A hierarchical volume-occupancy labeling scheme was created to simplify theorganization of atlas labels using volume and subvolumetric components. Segmentation rules weredeveloped to define boundaries that were not given explicitly in the atlas. The labeling scheme andsegmentation rules guided the segmentation and labeling of 160 contiguous regions within the atlas. Aunique three-dimensionai (3-D) database label server cailed the Talairach Daemon (http:/ /ric.uthscsa.edu/projects) was developed for serving labels keyed to the Talairach coordinate system. Civen an x-y-z
Talairach coordinate, a corresponding hierarchical listing of labels is returned by the server. The accuracy
and precision of the forward-transform labeling method is now under evaluatton. Hum. Brain Mappinglnc.
5:238-242, 1997. o 1997Wiley-Liss,
Key words: spatial normalization;
brain labelingsegmentation; Talairach Atlas; Talairach Daemon; naming hierarchy;

aaIN T R OD U C T IO N
Standardized three-dimensional (3-D) coordinates
based on the Talairach Atlas [Talairach and Tournoux,
19881are used with increasing frequency for analyzing

Contract grant sponsors: NIMH and NIDA; Contract grant number:
Human Brain Project P20 '}'4H/DA52176.
*Correspondence to:Jack L. Lancaster, Biomedical Image Analysis
Division, Research Imaging Center, University of Texas Health
Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio,
TX78284-6239.
Received for publication 7 May 1997; accepted BMay 7997

and reporting human neuroimaging research. Suchcoordinates provide precise, quantitative descriptionsof locations within an atlas enabling direct and consistent access to its anatomical labels [Fox, 1995]. There
are two, literally opposite, coordinate-based methods
to retrieve brain labels from an atlas for a brain image.
Both rely on spatial normalization to transform a brain
image volume to conform with features of a brain
atlas. In the first, or forward-transform method, brain
coordinates are used as indices to locations within abrain atlas to retrieve a corresponding label (coordinateIabel mapping). The forward method is common practice when using the 1988 Talairach Atlas, the Brain-

. Forward-Transform Brain Labeling I

TABLE l. Hierarchical volume-occupancy brain labelling scheme*

Structure1abel

Hierarchy level

Cerebrum(R/L)

Hemisphere (level 1)
Lobe (level 2)
Gyrus (level 3)

Tissuetype (level4)
Cytoarchitecture(level 5)

Lobes

Gyri
GMBA

WM
Tract

SubgyralWM
Tiact

C e r e b e l l um
Sublobar

Nuciei

Extranuclear

CSF
GM
WM
Space Subnuclear Tract

WM
Tract

Br ai n ste m(R/L)(R/L)

Tn nroprpss

Tn nroores sCSF
Space

*BA, Brodmanrr area; WM, white matter; GM, gray matter; CSF, cerebral spinal fluid

Mup@ database [Fox et a1.,7994],or other brain atlases[Roland et al., 7994f to get labels. For the second, orinverse-transform method, volumes of interest (VOIs)
are transformed from an atlas to conform to a brain
image volume (label-coordinate mapping). This method
is used by the ANIMAL software developed at Montreal Neurological Institute [Collins et al., 1995] andhas been implemented in various formats by others.
Each labeling strategy relies on accurate segmentation of labeled structures in an atlas brain, and both are
under development as part of the International Consortium for Brain Mapping (ICBM) project fMazziotta eta1.,19951.The forward method is based on the 1988
Talairach Atlas where the segmentation task is simplified, since labels already exist. Flowever, boundaries
are not well-defined for all labeled structures. The
inverse method is based on an individual highresolution MRI brain image volume that was transformed to conform to a standard atlas, and serves as a3-D surrogate for the original atlas. Segmentation
within this atlas volume would be impossible forsmaller structures such as Brodmann areas without
dependence on other atlases. Neither spatial normalization nor atlas segmentation can be performed withcomplete accuracy, though spatial normalization isconsidered to be more correct when using a surrogateatlas with characteristics similar to the subject brain
image [Collins et aL.,7994;Friston et aI.,1995],favoring
the inverse method. Conversely, segmentation is more
complete using a published atlas, since many morebrain regions are available, favoring the forward
method. These factors led to the study and development of both methods, a forward method based on the1988 Talairach Atlas and an inverse method based on
the atlas concept of ANIMAL. This preliminary report
describes the labeling scheme and segmentation rules
for a forward-transform method and its initial implementation for anatomical labeling.

MA TE R IA LS A N D ME TH ODS
Talairach Atlas labeling scheme
A volume-filling, hierarchical, anatomical-labelingscheme was used wherein anatomical structures aredefined as contiguous collections of voxels, each voxel
assigned an x-y-z coordinate (Tabte I). Though thisscheme was intended for the TalairachAtlas, its design
was purposely general so that it can be used as aguideline for labeling other atlases. The general scheme
is to subdivide a structure at level N into its comDonent substructures in level N-1. For example, the briin
is subdivided into its component volumetric structures(cerebrum, cerebellum, ind brainstem) in level 1.
Likewise, the cerebrum (in level 1) is subdivided into
its component structures (lobes and sublobar regions)in level 2. This scheme continues down to level 5 for
the Talairach Atlas. Failure to reach level 5 indicatesthe bounding level of the labeling scheme for a given
structural component. For example, there are no level 5structural components for white matter (WM) or cerebral spinal fluid (CSF), since no subcomponents areresolvable in the Thlairach Atlas. The naming hierarchyin Table I does not illustrate the full detail of names
used for lobes, gyri, nuclei, and subnuclear structures(see Talairach Daemon at hltp:/ /ric.uthscsa.edu/
projects for more details).
Talairach Atlas segmentation
Avolumetric image was synthesized from the Talairach Atlas with 1-mm3 voxels such that each voxel canbe addressed using Talairach coordinates. Axial section images from the atlas were digitized using anHewlett Packard Scanlet IIcx color scanner at a resolution of approximately 0.1 mm. Each brain structure (orI Lancaster et al. Iregion) was automatically extracted using thresholding techniques and manually detailed to correct forerrors. Labeled regions were reduced to a spatial
resolution of 1 mm. Nearest-neighbor interpolation
was used to provide data between the atlas sections.
Region labeling and extraction started at level 1 of thelabeling scheme by forming the cerebrum, cerebellum,and brainstem into 3-D volumes of interests (VOIs)
using Alice@ and Dipstation@ image processing software from the Hayden Image Processing Group (Boulder, CO). Common boundaries of VOIs were used in
the creation of VOIs in each successive level. This
greatly simplified the creation of new VOIs and wasconsistent with the volume hierarchy labeling scheme.
Preliminary rules for boundary definitions were developed where boundaries were not explicitly given in theatlas (e.g., Brodman areas) [Freitas et al., 7996]. These
rules have been great$ refined and are given for structures
within each hierarchical level of the cerebrum as follows:
Level l (Hemisphere)
Exterior boundaries defined using a convex hullof cortex
Medial boundaries manually outlined from atIas sections
Level 2 (Lobe)
Lobes
Exterior boundary identical to hemispheres
Interior boundary formed by lines connecting
deepest extremes
Sublobar (a single sublobar region was defined tofill remainder of volume within the hemisphere)
Level3 (Gyrus)
Gyri
Exterior boundary was identical to associatedlobeInterior boundary formed by lines connecting
deepest points of limiting sulci
Intragyral boundaries formed by lines bisecting
limiting sulci
Minor invaginations (without atlas labels) wereignored
Subgyral (a single subgyral region was defined tofill remainder of volume within lobes)
Nuclei (manually labeled following atlas)
Extranuclear (a single extranuclear region wasdefined to fill remainder of sublobar volume)
Level4 (Tissue Type)
Gray matter (GM) and white matter (WM) arereadily bounded within gyri
WM and cerebral spinal fluid (CSF) are readilybounded within subgyral regions; the CSF isfurther labeled by ventricle

GM and WM are readilybounded within nuclei
WM and CSF are readily bounded within extranuclear regions
Level 5 (Cytoarchitecture)
Brodmann areas (Interior and exterior boundaries are constrained to cortical GM; Brodmannintergyral boundaries are taken at the pit ofassociated sulcus; Brodmann intragyral boundaries selected to maintain continuity with othersections where landmarks are available)
Subnuclear structures are readilv bounded
Talairach Daemon
A unique 3-D coordinate-based database structure
was developed for serving brain labels. The core of thedatabase is a 3-D image array spanning the full extentof Talairach space at a resolution of 1 mm with x-y-z
dimensions of 770 x 200 x 210 mm. Each 1-mm3 voxel
within this 3-D array contains a pointer to a voxel
record that provides information about labels for thatvoxel. Each voxel record contains pointers to labels.
This scheme provides rapid indexed accessto any label
within this space. The database is accessedthrough aserver that accepts requests, performs the requested
query, and sends results to the requesting application.
Both the database and server are memory-resident andrun on a Sparcstation 20 Sun Microsystems (Mtn. View,CA). The database and server are called the Talairach
Daemon (TD) to indicate the brain standard used andthe memory-resident nature of the software. By design,
database transactions can be performed at extremely
high speeds. High throughput is achieved by keeping
the query and response data stream small and using
tcp /ip sockets for communications.
RESULTSThe volume-occupancy labeling scheme was used toguide the bounding of the Talairach Atlas for all level 1
structures as well as cerebrum structures for levels 2-5.
One hundred sixty structures have now been incorporated into the Talairach labels database served by theTalariach Daemon. Level 4 is not fully labeled, but thiswork is underway. Segmentation and label designation for levels 2-5 for the cerebellum and brainstem areplanned in the future.
The first-generation Talairach Daemon (TD) is now
fully operational. A ]ava applet is now available(http: / / ric.uthscsa.edu/projects) and provides easy
access to labels and overlays of structures in the TD
databases.An overview of the database and server isavailable at this site. The Talairach Daemon also

. Forward-Transform

Brain Labeling oFigure l.
Overlaysof Talairachlabelsfor frontal, temporal, and occipitallobesfor the transaxialsectionimage
from the TalairachAtlas at z : * | (backgroundadaptedfrom Talairachand Tournoux [ | 988]).

provides access to structure probability maps (SPmaps). In fact, it was specifically designed to provide
access to the more complex probability maps. These
data are a product of aP20 project seeking to develop a
probabilistic reference system for the human brain
fMazziotta et al., 1995]. Currently nine structures are in
the SP maps database.The SP maps provide a pr"obability of occurrence for each structure, given an x-y-z
coordinate. Graphic overlays are provided for eachstructure in the Talariach labels and SP maps databases. The overlays from the SP maps represent theregions where the SP map for a structure is 50% orlarger.
Figure 1 is taken from the atlas view window of theTD Java applet. It illustrates overlaying of several
lobes onto an electronically modified version of 1988
Talairach Atlas axial section images. The TD applet
also provides interactive access to Talairach labels,
formatted using the hierarchical volume-occupancy
scheme, as well as SP map labels and probabilities. As
the pointer is moved around in the atlas window, x-y-z
coordinates are displayed on the main window. Labels

. Lancaster et al. .

can be fetched by clicking the mouse button while
pointing at a site of interest in the atlas window. The
requested labels are printed to the main window withgood time response for most users (1-4 sec for mostsites around the world).
D ISC U S SION
This preliminary report expands a recently developed volume occupancy naming strategy and describes rules developed for segmentation and labeling.
The new volumetric segmentation rules and labelingscheme provide a natural method to hierarchicallyorganize and label a 3-D brain atlas. The accuracy of
the Talairach labels is dependent on the accuracy of thespatial normalization used to conform brain images tothe 1988 atlas brain. For spatial normalization methods
used today, it is anticipated that label conformance will
be good for structures in levels 1-3. The degree of label
conformance will diminish in level 4, but should bebetter for higher degree-of-freedom spatial normalization methods [Collins et a7.,7994; Friston et aI., 19951.
Finally, in level 5, where current spatial normalization
schemes fail, the labels should be used with caution. A
critical evaluation of the accuracy and precision of thislabel serving scheme is underway [Freitas et a1.,1996)
in high-resolution MR images spatially normalizedusing a modality-independent 9-parameter spatial normalization method [Lancaster et a1.,79951.
Plans are under development to incorporate thevolume occupancy naming scheme into the BrainMap
database using the Talairach labels database. Our
experience with the BrainMap database indicates thatthe anatomical labels from the Thlairach Daemon
should be more standardized (and therefore more
useful in queries) than conventional anatomical descriptions found within the human brain mapping literature. This is a good example of the use of the forwardtransform method for accessing anatomical labels. An
example of using the inverse-transformation for labeling is seen with the Talairach Daemon applet, wherethe various anatomical region labels are overlaid onto

the Talairach Atlas section images to show the full
extent of region labels.
The Talairach Daemon is currently accessed bynumerous sites around the world using the Javaapplet. Additionally, accessto the Talairach labels andSP maps databases is provided in the most recent
release of the BrainMap Search and View software(version 4.0 for Sun Solaris is available for download atour website). We are currently evaluating software fordirect access to the TD database using TD routines
incorporated into in-house software and as standalone command-line programs. Final versions of these
TD routines will be available at our website in thefuture.
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