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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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