Optimum Template Selection
Transcript of Optimum Template Selection
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Optimum template selection for atlas-based segmentation
Minjie Wu,a Caterina Rosano,b Pilar Lopez-Garcia,c
Cameron S. Carter,c and Howard J. Aizensteind,
aDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USAbDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USAcDepartment of Psychiatry, University of California at Davis, Sacramento, CA 95817, USAdDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh, 3811 OHara Street, Pittsburgh, PA 15213, USA
Received 21 March 2006; revised 21 June 2006; accepted 31 July 2006
Available online 26 December 2006
Atlas-based segmentation of MR brain images typically uses a single
atlas (e.g., MNI Colin27) for region identification. Normal individual
variations in human brain structures present a significant challenge for
atlas selection. Previous researches mainly focused on how to create a
specific template for different requirements (e.g., for a certain
population). We address atlas selection with a different approach:
instead of choosing a fixed brain atlas, we use a family of brain
templates for atlas-based segmentation. For each subject and each
region, the template selection method automatically chooses the best
template with the highest local registration accuracy, based on
normalized mutual information. The region classification perfor-
mances of the template selection method and the single template
method were quantified by the overlap ratios (ORs) and intraclass
correlation coefficients (ICCs) between the manual tracings and the
respective automated labeled results. Two groups of brain images andmultiple regions of interest (ROIs), including the right anterior
cingulate cortex (ACC) and several subcortical structures, were tested
for both methods. We found that the template selection method
produced significantly higher ORs than did the single template method
across all of the 13 analyzed ROIs (two-tailed pairedt-test, right ACC
at t(8)= 4.353,p = 0.0024; right amygdala, matched paired ttest t(8)>
3.175, p
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this structure. It has been estimated that approximately 3060%
of the population have a paracingulate sulcus (PCS, Paus et al.,
1996), a normal variant of the ACC, in which there is an
additional gyral fold. Wide variability can also be observed for
other cortical structures (Ono et al., 1990), which present a
fundamental challenge in selecting the best template for
automated anatomical labeling. A single brain template is unableto represent those regions with multiple normal anatomical
variations (e.g., ACC) and thus the performance of atlas-based
segmentation suffers.
Most previous research involving atlas-based segmentation
has used a single fixed template strategy (Dawant et al., 1999;
Vemuri et al., 2003; Carmichael et al., 2005). Several other
studies have demonstrated that specialized atlases are more
appropriate for particular populations, such as children or the
elderly (Thompson et al., 2001; Prastawa et al., 2005). In this
study we take a different approach. Instead of choosing a fixed
atlas such as Colin27 or a population-based atlas such as
MNI305, we use a family of brain templates and for each subject
we choose the best template for the automated anatomical
labeling. The intuition is that the variations in normal brainanatomy can be better represented as a small number of prototype
atlases (e.g., presence or absence of paracingulate) rather than as
a single average brain. For each subject, the template, which
gives the optimum localized registration for a specific ROI (using
a fully automated algorithm), is chosen as the optimum template
for the automated anatomical labeling. This approach has
previously been shown effective in atlas-based segmentation of
bee brain images (Rohlfing et al., 2004). In the current study,
this atlas selection technique was tested on two different human
brain image data sets based on the automated anatomical
labeling of multiple ROIs including right ACC, left and right
amygdala, caudate, hippocampus, pallidum, putamen, and
thalamus proper. For both data sets, the ROIs segmented using
the optimum template selection method and the standard single
template method were compared to the manual anatomical
tracings respectively.
Subjects and methods
Subjects
Two sets of data were used to evaluate the template
selection approach. Both data sets have been previously
described in greater detail: Data Set 1 (Wu et al., 2006) and
Data Set 2 (http://www.cma.mgh.harvard.edu/ibsr/). Brief de-
scriptions follow.
Data Set 1: Nine subjects (6 male/3 female; mean age 24.3,range 2032 years old; right-handed) participated. Scanning was
done on a 1.5T GE CVi scanner with 3D SPGR (TR/TE=5/25 ms,
flip angle= 40, FOV= 24 18 cm, slice thickness = 1.5 mm, matrix
size=256192). The data were originally acquired with a voxel
size of 0.94 0.94 1.5 mm3, and were resampled to a voxel size of
1 1 1 mm3.
Two raters independently and manually classified the right
ACC on each subject, which were used as the gold standard
(i.e., best estimated) region mask. Right ACC tracings were
made on serial coronal slices. The sagittal and axial views were
used as a reference to outline the ACC. The posterior limit of
the ACC was defined by a vertical line perpendicular to the
anterior commissureposterior commissure (ACPC) plane and
passing through the AC. The cingulate and callosol sulci
constituted the outer and inner boundary respectively. When a
sulcus running parallel and superior to the cingulate sulcus was
present, the paracingulate gyrus was included in the tracing.
Inter-rater reliability for the right ACC manual tracings of the
two raters was calculated using the intraclass correlation
coefficient (ICC). The ICC for the right ACC was 0.97. Toobtain intra-rater reliability, a subset of 5 MR images was
retraced by the same rater after 34 weeks (mean 22.2
3.4 days). The ICC for intra-rater reliability for the right ACC
was 0.93.
Data Set 2: Thirteen T1-weighted MR brain images from
Internet Brain Segmentation Repository (IBSR) and their manual
tracings were provided by the Center for Morphometric Analysis at
Massachusetts General Hospital, available athttp://www.cma.mgh.
harvard.edu/ibsr/. Brain images were acquired from healthy control
subjects. The 13 subjects from IBSR were at coronal orientation,
with a m atrix of 256 256 128, a nd had voxel siz es of
0.84 0.84 1.5 mm3 or 0.94 0.94 1.5 mm3 or 111.5 mm3.
The T1-weighted images were positionally normalized into the
Talairach orientation (rotation only) and then processed withbiasfield correction routines. The voxel size on the processed
images was unchanged by the reorientation and biasfield
correction. The manual tracings were all done by experts at the
Center for Morphometric Analysis of Massachusetts General
Hospital, Harvard Medical School. Iso-intensity contours were
used in the manual tracing to define the primary borders of
anatomical structures (Filipek et al., 1994).
The right ACC was manually traced on Data Set 1, and
multiple ROIs including the left and right amygdala, caudate,
hippocampus, pallidum, putamen, and thalamus proper were
manually traced on Data Set 2. The manual tracings of both
data sets served as gold standard tracings for evaluating the
automatically segmented results. The manual anatomical tracings
of the subject also served as the atlas when the subject brain
image was used as the template.
Registration method
Atlas-based segmentation labels the anatomical regions on
individual images by registering the template image of the brain
to the individual brain image. We refer to the registration method
we use as the Automated Labeling Procedure (ALP). This is
derived from the methods used by Chen (1999), which consists
of preprocessing steps (including skull stripping and cropping)
and a series of registration techniques including hierarchical
registration (Unser et al., 1993) and demons based registration
(Thirion, 1998). The ALP starts with 12 parameter affineregistration to correct global differences such as orientation and
brain size, and goes on with a grid-based piecewise linear
registration for coarse alignment, then finally uses demons
registration algorithm as a fine-tuning for a voxel-level spatial
deformation. We have implemented this method using the
registration library from the Insight Segmentation and Registra-
tion Toolkit (ITK, Yoo, 2004). The performance of this method
was quantitatively compared to popular registration packages
including Automated Image Registration (AIR, Woods et al.,
1998) and Statistical Parametric Mapping (SPM, Ashburner and
Friston, 1999), and was found to perform significantly better.
Detailed descriptions and evaluations of the ALP are presented in
Wu et al. (2006).
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Template selection method
The flow chart of the template selection algorithm is shown
in Fig. 1. To select the optimum template for the subject and
ROI, each subject is first registered to each template, which
creates a warped image (templatesubject); then, for each
ROI, the registration accuracy of each template is evaluatedbetween the warped image and subject image at a standard local
region area O. This common area is formed as the disjunction
image of the segmented ROIs Oi from all the templates. The
local area O O1[ O2: : : [ On, with Oi as the segmented ROIusing ith template for the subject.
Normalized Mutual Information (NMI) is then used as the
metric to measure the local registration performance:
NM Ix;y Hx Hy=Hx;y 1
where
Hx Xk
i1
Pixlog2Pix 2
Hy Xk
i1
Piylog2Piy 3
Hx;y Xkx
ix1
Xky
ix1
Pix; iylog2Pix; iy 4
where x is the cropped local ROI image of each warped image
(each templatesubject) and y is the cropped ROI image of the
subject at the same local area; H(x), H(y) are the entropies and
H(x,y) is the joint entropy ofxandy. NMI describes the similarity of
the warped image and the target image at a local ROI area, whichalso evaluates local registration accuracy and the performance of the
template in the classification of the ROI. The template, which gives
the maximum local NMI, is chosen as the locally optimized
template for each subjects ROI.
Template evaluation methods
Overlap ratio (OR) is used to quantify the region classifica-
tion quality of each registration. OR is defined as the ratio ofoverlapping voxels to total voxels, as given below:
overlap ratio volBB
volB [B
where B is the manually traced ROI, and B is the automatically
segmented ROI on the subject. A perfect overlapping of the
manually traced ROI B and the automated labeled ROI B will
lead to an OR= 1, while less overlap will result in a smaller
value (0OR1).
For each data set, the segmentation performance of the
individual atlas was evaluated based on a leave-one-out
approach. Each of the 9 subjects from Data Set 1 was chosen
to serve as an individual template and was registered to the
remaining 8 subjects. Nine subjects out of the 13 subjects for
Data Set 2 were randomly chosen as individual template and
registered to the remaining 12 subjects. For each ROI, the
performance of a single atlas was evaluated as the mean OR of
the automatically labeled ROI against the manual traced ROI
across multiple subjects using the same atlas. The average
performance of the single template strategy was measured as the
average OR across multiple templates. Additionally, the standard
MNI (Montreal Neurological Institute) brain Colin27 (Holmes et
al., 1998), which carries high anatomical details and has a high
spatial resolution (1 1 1 mm3 voxel size), was also used as
the template to segment the right ACC on Data Set 1 for
comparison.In the template selection algorithm, a subject in data set 1 was
warped to the 8 remaining atlases and the atlas with the best local
registration accuracy was chosen; for consistency, the atlas was
selected from a randomly chosen 9-subject subset of data set 2.
The performance of the optimum template was measured as the
mean OR across all the combinations of available templates for
the ROI classification.
The performances of the optimum template selection method
and the single template strategy were also evaluated respectively
by the absolute volum e agreem ents betwe en autom atic ally
segmented ROI and hand drawn ROI. Volume agreement between
the automatically segmented ROI and the hand drawn ROI was
measured in SPSS (Nie et al., 1970) using single measure ICC,
with two-way mixed model and measures of absolute agreement.For the individual template case, the automatically labeled ROIs
from each template were compared against the hand drawn ROIs
across all subjects using ICC and the average ICC across all the
template for segmenting the same ROI was used as the average
performance of single template method.
Results and discussion
The evaluations on the automated anatomical labeling results
from both methods (single template and optimum template) were
compared for each data set. Both methods used exactly the same
pathway for the registration, as well as the same thresholds (to
Fig. 1. Template selection flowchart. The processing steps that constitute
the template selection model, which is used to choose the optimum
template from a family of templates (Tk) for the segmentation of ROI (R)
on a subject (S).
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remove the edges of the automated anatomical segmentations). The
only difference is that they used different atlas selection strategies.
We found that for most of the ROIs the optimum template
produced significantly and consistently better classification results
compared to the single template method.
Data set 1
As predicted, atlas-based segmentation with the template
selection method produced significantly better mean ORs for right
ACC classification across the 9 subjects than with any single
template. The mean ACC OR when registering with a single
template ranged from 42.7% to 52.7% (mean OR 49.5%), and the
mean OR using the standard template MNI Colin27 was 47.3%;
while the mean OR with the optimum template selection method
was 54.7% (8 templates); this method reached an OR of 57.7%
when an optimized template subset was used (3 templates,Fig. 2).
As seen in Fig. 2, the performance of the optimum template
selection method was better than the performance of any of the
template candidates or the standard MNI template Colin27. A two-
tailed pairedttest showed that the registration result from optimumtemplate (8-template case) based method was significantly better
than the results from single templates at t(8)=4.353,p =0.0024.
One subject image, 3 template images and the corresponding
NMI results from the template selection model are shown inFig. 3.
It can be noted from the figure that the target subject has a
paracingulate sulcus (in red), and that template 2, which has a
similar paracingulate sulcus (in red) was automatically selected as
the optimum template to segment the right ACC on the subject
using the maximum NMI between the warped image and the target
subject image at local ACC areas. The OR of the automatically
labeled right ACC from the 3 templates against the manual tracing
on the target image was also calculated and compared to validate
the performance of the template selection model.
It is important to determine how many templates are needed to
achieve robust automated anatomical labeling of certain ROIs with
the template selection method. In the previous test, for each subject
the remaining 8 brain images were used as template candidates for
the classification of right ACC. To test how the performance of the
template selection method changes with the number of templates,
and to decide how many templates are sufficient for the
classification of specific ROI, we tried the template selection
method on this dataset using all subsets of the 8 different templates
(from 1 to 8 templates in each subset). The performance of the
template selection method with a particular number of template
candidates was estimated as the average OR across all subjects for
all subsets of templates of that cardinality. For example, the
performance of the template selection method with 2 templates wasestimated as the mean OR across the 7 subjects (excluding the 2
templates) and across all possible template combinations (C92=36).
The mean ORs are plotted in Fig. 4 against the number of
templates used. As expected, as the number of templates increased,
the average performance of the template selection method
improved. For each subset of templates (N templates), the
performance was estimated as the average OR across subjects
(9-N). We observed that among the template subsets of the same
cardinality, the template set with the widest anatomical variations
performed better than other combinations of templat es. For
example, in the automated classification of right ACC, we
discovered there was a sufficient set of three templates, which
included three prototypes: one template with a paracingulate sulcus,
one with a thick anterior cingulate cortex, and the third with a thinanterior cingulate cortex, which had the best performance and
achieved an average OR of 0.577 (similar to that of the 8 template
case), as shown inFig. 3. This suggests that in addition to template
number, template variability is also important, such that with an
appropriately variable set of templates, a fewer number of templates
are sufficient for high accuracy.
Data set 2
The automated anatomical labeling results for multiple regions
(left and right amygdala, caudate, hippocampus, pallidum, puta-
men and thalamus proper) on the IBSR dataset using the template
selection method and the single template method were compared to
manual tracings respectively using OR and volume agreement. For
most of the ROIs, the template selection method consistently
provided more reliable region classific ation than the single
template method. The mean percent ORs and ICCs between the
estimated volumes and manual traced volumes for both methods
are shown inFig. 5.
For data set 2, the template selection method gave a higher
mean OR than the single template method for all ROIs; the OR
percent increase ranged from 4.4% to 13.1% (mean increase 8%).
The differences in ORs were highly significant for all ROIs (for
right amygdala, matched pair t(8)>3.175, p 4.36, p
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caudate, while previous research reports inter-rater reliability of
0.94 for the left caudate and 0.95 for the right caudate
(Venkatasubramanian et al., 2003).
To illustrate the absolute agreement between the estimated
volumes and the manual volumes, the estimated volumes of four
ROIs on data set 2 from both methods were plotted against the
manual volumes in Fig. 6. As Fig. 6 illustrates, the accuracy of
volume estimates improved considerably by using the optimum
template method. Overall, the optimum template method provided
better volum e estimates than the single template method. However,
for the left and right amygdala, neither method gave a good
estimate. This is because the registration failed to provide an
accurate region classification with any of the template candidates.
Conclusion
In this study, multiple prototype atlases were used to address
the normal brain anatomy variations in the atlas-based segmenta-
tion of MR brain images.
The template selection algorithm uses normalized mutual
information to choose the template (from a family of templates)
that gives the best local registration accuracy. This template
selection model is of special use to those regions with high
variability across subjects such as cortical structures (Ono et al.,
1990), where a single template cannot readilycapture the variability.
We found that the template selection model produced significantly
better ORs and more reliable volume estimates in the analyzed
multiple ROIs than the single template strategy. The segmentation
Fig. 3. For data set 1 the optimum template selection model selects the best template from a family of 8 templates to segment the right ACC on the target image.
Three of the templates and the target subject images are shown here; the hand-drawn ACC on the subject and templates are also displayed in color (cingulate in
blue and paracingulate in red). Also shown are the normalized mutual information (NMI) calculated by comparing the warped template with the target image, and
the overlap ratios (ORs), calculated by comparing the automated labeled result with the manual tracing.
Fig. 4. The performance of the multiple template method with different
number of templates. The mean ORs across all the combinations of same-
number templates were plotted against the number of templates used.
Fig. 5. Comparison of the reliability of the automated ROIs using the
template selection method and using a single template. Key: LAleft
amygdala, RAright amygdala, LCleft caudate, RCright caudate,
LHleft hippocampus, RHright hippocampus, LPaleft pallidum,
RParight pallidum, LPuleft putamen, RPuright putamen, LTleft
thalamus proper, RTright thalamus proper. Top: mean percent OR
comparion. Bottom: the intraclass correlation coefficients (ICCs) of
volume agreement for both methods.
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accuracy improved withthe increasingnumber of the templates used
in the template selection method. In addition to the template number,
the anatomical variability within the used templates is also
important, such that a fewer number of templates with appropriate
anatomical variations are sufficient to achieve high accuracy in the
atlas-based segmentation; in the case of right ACC, we found a set of
3 prototype templates with wide anatomical variations, which
performed better than other template combinations.
This higher registration accuracy with the template selection
model is achieved at the cost of higher computation load. Multiple
non-rigid registrations are required in order to evaluate the perform-
ance of multiple templates. Also although this method produced
improved anatomical classification accuracy for all the analyzedROIs, it did not give satisfactory region estimates for the left and
right amygdala. Alternative classification methods (perhaps using
feature-based registration) should be used to improve the auto-
matical labeling of the small and difficult to segment regions such
as the left and right amygdala. Also more advanced method may
be needed to evaluate the performance of the templates at such
regions.
The number of available atlases limits the registration accuracy.
More atlas candidates may bring higher registration accuracy, but
with extra computation load, since in order to evaluate the templates
we need to register each template to the target subject. Also the
numberof atlases needed is related to the normal anatomic variations
of the region to be segmented. Different ROIs may require different
numbers of atlas prototypes for the automated anatomical
classification. In this study, we discussed the possible atlas
prototypes for the anterior cingulate cortex (thin ACC, thick ACC,
and with a paracingulate sulcus). Further research is needed to
explore the normal variations of the brain anatomy.
Multiple templates are needed in the template selection method.
The templates can either be manually traced locally by experts, or
downloaded from a public database. There are many manually
labeled atlases available online, such as the IBSR dataset used in
this paper (http://www.cma.mgh.harvard.edu/ibsr/), which has 18
high-resolution T1-weighted MR image data with expert segmen-
tations of 43 individual structures.
This method chooses the best atlas from a family of atlases foreach subject and ROI, and is independent of the registration
techniques. In our study, we used the deformable automated
labeling pathway for the inter-subject registration, and it can be
easily accommodated into alternate available pathways such as
Automated Image Registration (AIR) or Statistical Parametric
Mapping (SPM).
Acknowledgments
This research was supported by NARSAD, The Pittsburgh
Foundation, Burroughs Wellcome Translational Scientist Award
(CSC), and NIMH grants K02-MH064190, K23 MH064678 P30
MH052247 and NIA grant P30 AG024827, Pittsburgh Claude D.
Fig. 6. Region classification performance of the optimum template method and the single template method. The absolute voxel numbers of segmented ROIs from
both methods are compared to manual segmentation respectively using the linear regression model. Four ROIs (left and right caudate, and left and right thalamus
proper) were analyzed for 13 subjects. Subject 6 was used as the atlas in the single template method; the optimum template was chosen from a 9-subject subset.
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Pepper Older Americans Independence Center. We thank the
IMAGe (Imaging Methods and Analysis in Geriatrics) group and
the Clinical Cognitive Neuroscience laboratory at the University of
Pittsburgh for their assistance.
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