Parametric Surface Modeling and Registration for Comparisonof Manual and Automated Segmentation of the Hippocampus

Li Shen,1,2* Hiram A. Firpi,1 Andrew J. Saykin,1 and John D. West1

ABSTRACT: Accurate and efficient segmentation of the hippocampusfrom brain images is a challenging issue. Although experienced ana-tomic tracers can be reliable, manual segmentation is a time consumingprocess and may not be feasible for large-scale neuroimaging studies. Inthis article, we compare an automated method, FreeSurfer (V4), with apublished manual protocol on the determination of hippocampal boun-daries from magnetic resonance imaging scans, using data from an exist-ing mild cognitive impairment/Alzheimer’s disease cohort. To performthe comparison, we develop an enhanced spherical harmonic processingframework to model and register these hippocampal traces. The frame-work treats the two hippocampi as a single geometric configuration andextracts the positional, orientation, and shape variables in a multiobjectsetting. We apply this framework to register manual tracing and Free-Surfer results together and the two methods show stronger agreementon position and orientation than shape measures. Work is in progress toexamine a refined FreeSurfer segmentation strategy and an improvedagreement on shape features is expected. VVC 2009 Wiley-Liss, Inc.

KEY WORDS: shape analysis; segmentation; registration;hippocampus

INTRODUCTION

The hippocampus has been extensively studied with neuroimagingtechniques given its importance in learning and memory and its poten-tial as a biomarker for brain disorders such as Alzheimer’s disease (AD)(Thompson et al., 2004; Csernansky et al., 2005; Saykin et al., 2006;Wang et al., 2007), epilepsy (Hogan et al., 2006), and schizophrenia(Gerig et al., 2001; Csernansky et al., 2002; Shenton et al., 2002).Although some groups used automated methods for quantification ofthe size and shape of the hippocampus (Csernansky et al., 2002; Shenet al., 2002; Csernansky et al., 2005; Hogan et al., 2006; Wang et al.,2007), in many studies (Gerig et al., 2001; Shenton et al., 2002;Thompson et al., 2004; Saykin et al., 2006; McHugh et al., 2007;Yushkevich et al., 2007), hippocampal segmentation from magnetic reso-

nance imaging (MRI) scans was done manually byanatomic tracers using software tools that were eitherin-home made or publicly available (e.g., BRAINS(Iowa Mental Health Clinical Research Center, 2008),3D Slicer (NAMIC, 2008), ITK-SNAP (Yushkevichet al., 2006)). Many of these tools provide manualtracing capabilities and semiautomatic approachesinvolving less human interaction.

Accurate and efficient segmentation of the hippo-campus from brain images is still a challenging issue.Although experienced anatomic tracers can be reliable,manual segmentation is a time consuming process andmay not be feasible for large-scale neuroimaging stud-ies. For example, the Alzheimer’s Disease Neuroimag-ing Initiative (Mueller et al., 2005) collects 1.5T struc-tural MRI for over 800 subjects every 6 or 12 monthsfor 2–3 yr. Manual segmentation is apparently not anideal method for handling such a study involvingthousands of MRI scans. A feasible strategy for hippo-campal segmentation in large-scale studies should beable to minimize human intervention in the process-ing pipeline. Diffeomorphic mapping is a notablemethod for automatic segmentation and has beenused by Csernansky and colleagues in many hippo-campal studies (Csernansky et al., 2002, 2005; Hoganet al., 2006; Wang et al., 2007). FreeSurfer (Daleet al., 1999; Fischl et al., 1999, 2002) is an automaticsoftware tool for whole brain segmentation and corti-cal parcellation. Since FreeSurfer is freely available onthe web, it has been widely used in the neuroimagingfield. Two recent studies reported similar results oncomparing hippocampal volumes measured using theirown manual method and a specific version of FreeSur-fer (V4 in Shen et al., 2008, V3.04 in Tae et al.,2008): the intraclass correlation coefficients were bothbetween 0.8 and 0.85, showing good agreement.

In this article, we focus on comparing hippocampalmorphometric features beyond the volume determinedby the two methods to evaluate the reliability of Free-Surfer on extracting these features. We develop anenhanced spherical harmonic (SPHARM) processingframework to model and register the hippocampaltraces. The framework treats the two hippocampi as asingle configuration and extracts the positional, orien-tation, and shape variables in a multiobject setting.The proposed registration algorithm operates directlyon the SPHARM coefficients and thus is not onlyeffective but also efficient. We apply this framework

1Division of Imaging Sciences, Department of Radiology, IU Centerfor Neuroimaging, Indiana University School of Medicine, Indianapolis,Indiana; 2Center for Computational Biology and Bioinformatics, IndianaUniversity School of Medicine, Indianapolis, IndianaGrant sponsor: NIH, Foundation for the NIH; Grant number: NIBIB/NEIR03 EB008674-01, NIA R01 AG19771, NCI R01 CA101318, U54EB005149; Grant sponsor: Indiana Economic Development Corporation(IEDC); Grant number: #87884.*Correspondence to: Li Shen, Division of Imaging Sciences, Departmentof Radiology, Center for Neuroimaging, Indiana University School ofMedicine, Indianapolis, IN 46202, USA. E-mail: shenli@iupui.eduAccepted for publication 9 March 2009DOI 10.1002/hipo.20613Published online 29 April 2009 in Wiley InterScience (www.interscience.wiley.com).

HIPPOCAMPUS 19:588–595 (2009)

VVC 2009 WILEY-LISS, INC.

to register all the hippocampal data together and to decouplethe position, orientation, and shape for comparing the manualand FreeSurfer results. The proposed shape modeling and regis-tration framework can also be used as a general purpose toolfor other shape comparison and analysis applications.

MATERIALS AND METHODS

In this work, we use a data cohort for studying memory cir-cuitry in mild cognitive impairment (MCI) and early AD. Inthis MCI/AD cohort, there are hippocampal data available for123 subjects total, in four categories: 39 healthy older adults,38 euthymic older adults with cognitive complaints but intactneuropsychological performance, 36 older adults with amnesticMCI, and 10 adults with AD. Volumetric structural MRI datawere acquired on a 1.5 Tesla GE LX Horizon scanner using aT1-weighted SPGR coronal series with 1.5 mm slice thickness.Further details about this data set are available in Saykin et al.,2006.

Hippocampal and intracranial boundaries were obtainedusing (1) a manual protocol reported in McHugh et al., 2007using the BRAINS software package (Iowa Mental Health Clin-ical Research Center, 2008), and (2) a fully automated methodusing the FreeSurfer V4 package (Dale et al., 1999; Fischlet al., 1999, 2002). For manual segmentation, images werereformatted into isotropic 1-mm voxels and resampled into theplane perpendicular to the long axis of the hippocampus usingBRAINS. Manual traces were performed in the coronal planeusing markings placed in the axial and sagittal views to guideboundary determination. A 3D binary image was reconstructedfrom each set of 2D traces. Further details about this manualprotocol are available in McHugh et al., 2007. For automatedsegmentation, FreeSurfer was used to automatically labelsubcortical tissue classes using an atlas-based Bayesian segmen-tation procedure (Fischl et al., 2002). We ran the completeFreeSurfer pipeline without any manual intervention on anIBM HS21 Bladeserver cluster running Red Hat Linux. Eachslice of segmentation label map was inspected by a technicianand passed a basic test of quality control. From each label map,we extracted the left and right hippocampi as two 3D binaryimages. Figure 1 shows sample manual and Freesurfer results.For convenience, we use MT to indicate the manual tracingmethod and FS to indicate the FreeSurfer method.

We recently compared hippocampal volume determined bythe MT and FS methods (Shen et al., 2008). Since regional dif-ferences in shape are more likely to reveal clues to pathophysi-ology than volume, it is critical to examine differences inextracted shape features using various methods. This article isfocused on examining these morphometric features. We use theSPHARM description (Brechbuhler et al., 1995) to model thehippocampal surfaces and develop an enhanced SPHARMregistration algorithm to facilitate the comparison between theMT and FS data. This method is designed for comparing seg-mentation methods performed in two different image spaces

(e.g., in our MT and FS cases) without knowing their spatialrelationship. If this relationship is known, these two spaces canbe easily registered together and direct 3D volume comparisonas proposed in Fischl et al., 2002 can be applied.

SPHARM (Brechbuhler et al., 1995) is a highly promisingsurface modeling method that has been successfully applied tonumerous applications in brain imaging. SPHARM is used inthis study for modeling all the hippocampal surfaces. Its first stepis to create a continuous and uniform mapping from the objectsurface to the surface of a unit sphere. The result is a bijectivemapping between each point v on a surface and a pair of spheri-cal coordinates u and /: vðu;/Þ ¼ xðu;/Þ; yðu;/Þ; zðu;/Þ½ �T .The object surface can then be expanded into a complete set ofSPHARM basis functions Yl

m, where Ylm denotes the SPHARM

of degree l and order m. The expansion takes the form:vðu;/Þ ¼ P1

l¼0

Plm¼�1 c

ml Y

ml ðu;/Þ, where cml ¼ ðcmlx ; cmly ; cmlz ÞT .

The coefficients clm can be estimated up to a user-desired degree

by solving a set of linear equations. The object surface can bereconstructed using these coefficients, and using more coeffi-cients leads to a more detailed reconstruction (Fig. 2). Thedegree one reconstruction is always an ellipsoid. We call it thefirst order ellipsoid (FOE).

Surface registration aims to register all the models into acommon reference system to facilitate shape comparison. It cre-ates a normalized geometric representation to describe theshape after normalizing the size, position, and orientationmeasures (i.e., excluding scaling, translation, and rotation).Since the relative position and orientation between the two hip-pocampi (or the pose of the hippocampal pair) are of our inter-est, we treat the two hippocampi as a single configuration andstudy SPHARM registration in a multiobject setting. Scalinginvariance can be achieved by multiplying the coefficients by ascaling factor to normalize a certain volume. We normalize theintracranial volume (ICV) to account for the brain size varia-tion: (1) the mean ICV (5 1,421.2 cm3) of the MT data iscalculated, and (2) each hippocampal pair in both MT and FSdata is scaled proportionally along with the corresponding ICVso that the ICV is normalized to 1421.2 cm3. While ignoringthe degree zero coefficient results in translation invariance for asingle SPHARM model, in our multiobject setting, new meth-ods need to be developed to preserve the relative positionamong objects while removing the global translation effect.The surface registration methods described below aim toremove the effects of global translation and global rotation foraligning two multiobject complexes together.

The correspondence between SPHARM models is impliedby the underlying parameterization: two points with the sameparameter pair (u, /) on two surfaces are defined to be a corre-sponding pair. To register multiobject complexes, traditionalmethods (Shen et al., 2004; Styner et al., 2006) rotate theparameter net of each individual model to a canonical positionon its FOE for establishing the surface correspondence (Fig. 3),and then use the rigid-body Procrustes method to align recon-structed surface samples. These methods work well only if theFOE is a real ellipsoid (e.g., hippocampal case) but not anellipsoid of revolution or a sphere. To remove this restriction,

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we developed a registration method by minimizing the rootmean squared distance (RMSD) between two SPHARM mod-els instead of aligning the FOEs (Shen et al., 2007). Here, weextend this work to handle multiobject complexes.

Our approach shares a similar idea with the iterative closestpoint (ICP) method (Besl and McKay, 1992). We start with aninitial alignment and alternately run the following two steps torefine the alignment until it converges: (1) object space registra-tion for aligning corresponding surface parts and (2) parameterspace registration for refining the surface correspondence.

Suppose that we want to register a multiobject complex X toan atlas A. To create an initial alignment, we can first align Xto A in the object space and then rotate the parameter net ofeach SPHARM model in X to best match its counterpart in A.In hippocampal cases, the initial alignment can be establishedby applying the FOE method. In cases where the FOE methoddoes not apply, we can use ICP for initial alignment (referShen et al., 2007).

In object space registration, we aim to improve the align-ment in the object space. Since an initial surface correspon-dence has been created, we can simply create correspondingsurface samples between X and A and then align two corre-sponding point sets in a least squares sense (Besl and McKay,1992). We call this method as CPS (i.e., aligning correspond-ing point sets). Let T be the vector that translates the X centerto the A center, and R be the rotation matrix returned by CPS.We use the following approach to apply T and R to eachSPHARM model X and derive a new SPHARM representationthat matches A. Let vðu;/Þ ¼ P1

l¼0

Plm¼�1 c

ml Y

ml ðu;/Þ be a

SPHARM model. After applying translation T and rotation R,the new coefficients cl

m(T, R) can be calculated as follows: (1)c00ðT ;RÞ ¼ c00 þ T 3 2

ffiffiffip

p, and (2) cml ðT ;RÞ ¼ R3 cml for l,

m > 0.In parameter space registration, we aim to improve the sur-

face correspondence between SPHARM models that areroughly aligned in the object space. Since the underlyingparameterization defines the correspondence between differentSPHARM surfaces, our task is to rotate the parameterization ofone model to best match the others. The goodness of thematch is measured by the RMSD between two models. RMSDcan be calculated directly from SPHARM coefficients. Let S1

and S2 be two SPHARM surfaces, where their SPHARM coef-ficients are formed by c1,l

m and c2,lm, respectively, for 0 � l � lmax

and 2l � m � l. The RMSD between S1 and S2 can be calcu-

lated as: RMSD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1=ð4pÞPLmax

l¼0

Plm¼�1 jjcm1;l � cm2;l jj2

q. We

use a sampling-based strategy that fixes one parameterizationand rotates the other to optimize the surface correspondence byminimizing the RMSD. The rotation space can be samplednearly uniformly using icosahedron subdivisions. A naive solu-tion for rotating the parameterization of a SPHARM model isto recalculate the coefficients using the rotated parameteriza-tion. However, it requires solving three linear systems and istime-consuming. To accelerate the process, we use a rotationalproperty in the harmonic theory and rotate SPHARM coeffi-cients without recalculating the expansion. Details are availablein Shen et al., 2007.

While traditional registration methods (Shen et al., 2004;Styner et al., 2006) derive sampled point distribution modelsas results, our algorithm operates directly on the SPHARMcoefficients and so the results are still SPHARM models. Thisleads to a better potential of enhancing the registration resultsin addition to having a capability of performing subsequentanalyses in both spatial and frequency domains. Figure 4 showsa sample registration procedure, and we can see that our algo-rithm can further improve the FOE registration result with areduced RMSD.

RESULTS

We compared hippocampal volume between the MT and FSdata in Shen et al., 2008. In this work, we aim to examine

FIGURE 1. Sample segmentation results generated by manual tracing (left) and FreeSurfer(right): Each hippocampus is described by a binary image and the corresponding voxel surface is dis-played. Left hippocampi are shown in green and right in red. [Color figure can be viewed in theonline issue, which is available at www.interscience.wiley.com.]

FIGURE 2. Sample SPHARM reconstructions: Shown fromleft to right are original surface and SPHARM reconstructions upto degrees 1, 5, and 15.

590 SHEN ET AL.

Hippocampus

additional morphometric features. Thus, we first exclude vol-ume from all the MT and FS data: Each hippocampal pair isscaled proportionally along with the corresponding ICV so thatthe ICV is normalized to 1421.2 cm3 (i.e., the mean ICV ofthe MT data). The subsequent analyses are performed on thedata after this scaling normalization.

Atlas Generation and Data Registration

We first use all the healthy controls (n 5 39) in the MT datato create an atlas that represents an average hippocampal pair.Our method is as follows: (1) let the atlas be the first hippocam-pal pair; (2) register each pair to the atlas; (3) let the atlas be themean of all the data; (4) repeat (2) and (3) until the atlas con-verges. The left side of the atlas is shown in Figure 4a.

Our overall strategy for aligning the MT and FS datatogether is as follows: (1) register all the MT data to the atlasand (2) register each hippocampal pair in the FS data to itscounterpart in the MT data. We consider two types of regis-tration: a global one and a local one (Styner et al., 2006). Inglobal registration, we directly apply the algorithm proposed

in the Methods Section for aligning multiobject complexes.This rigid-body algorithm excludes only global translation and

rotation and so the spatial relation between the two hippo-campi or the pose of the hippocampal pair is preserved. Inlocal registration, each individual hippocampus is allowed to

match its template separately. This method expects to derive abetter RMSD by allowing local translation and rotation and

may account for possible non-rigid-body transformationeffects.

We use RMSD to measure the registration error between anindividual and its template. Figure 5 shows sample global andlocal registration results for the MT and FS hippocampal tracesof a same subject. Although both methods can derive goodregistration results, the local method does generate betterRMSDs. Table 1 shows a comparison of applying various regis-tration methods on the MT and FS data, where each entryrecords (Mean 6 SD) of the registration errors for all subjectsin a particular data set by a certain method. For either of globaland local strategies, we compare three methods: (1) FOE-PRM:rotating parameterization to a canonical position using FOEs,

FIGURE 4. Sample registration procedure: (a) The atlas. (b)The initial configuration of an individual, where the surface corre-spondence to the atlas has been established by FOE. (c) Resultof object space registration. (d) Result of parameter space registra-

tion. RMSD (in mm) is shown. The parameter net is shown asa colorful mesh on the surface. [Color figure can be viewed inthe online issue, which is available at www.interscience.wiley.com.]

FIGURE 3. Using first order ellipsoids (FOEs) to establish sur-face correspondence between two subjects: Each row correspondsto one subject, where both FOEs and the degree 15 SPHARMreconstructions are displayed. The underlying parameterizationdefines the correspondence between SPHARM models and isshown as a colorful mesh superimposed onto the surface. Shown

in (a) is the initial configuration of each subject, where the param-eter nets are not aligned well. Shown in (b) is the result of rotatingthe FOEs to a canonical position to establish surface correspon-dence across subjects. [Color figure can be viewed in the onlineissue, which is available at www.interscience.wiley.com.]

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(2) FOE-OBJ: one run of object space registration after FOE-PRM, and (3) OUR-ALG: our algorithm proposed above.Although the traditional SPHARM registration methods work

well on hippocampal data (refer the FOE-OBJ columns fortheir results), our algorithm is able to further improve theseresults (refer the OUR-ALG columns).

FIGURE 5. Global registration (a) and local registration (b)for the same subject. In each of (a) and (b): Shown in the firstrow are (1) the atlas to which the MT model is registered, (2) theinitial MT model, and (3) the registered MT model and shown inthe second row are (4) the MT model to which the FS model is

registered [i.e., the same as (3)], (5) the initial FS model, and (6)the registered FS model. RMSDs (in mm). The underlying parame-terization is shown as a colorful mesh on each surface. [Color fig-ure can be viewed in the online issue, which is available atwww.interscience.wiley.com.]

592 SHEN ET AL.

Hippocampus

Position, Orientation, and Shape

Although global registration excludes global effects on trans-lation and rotation, the spatial relationship between the twohippocampi is preserved. We use the globally registered FOEmodel to define this local pose information, including the posi-tion and orientation of each hippocampus. Let h be a hippo-campus, SPh be its SPHARM model that is registered in theatlas space, Ph be the FOE center of SPh, and Nh be the FOEnorth pole of SPh (refer Fig. 3 for sample FOEs on whichnorth poles are shown as yellow dots). We define Ph to be theposition measure of h and Oh 5 (Nh2Ph)/|Nh2Ph| to be theorientation measure of h.

We report our work on comparing the position and orienta-tion measures determined by the MT and FS methods. We usethe position measure of the left hippocampus as an example toshow our approach. Let S be our subject set. Given x 2 S, we

use lxM to denote its left hippocampus determined by the M

method, where M 2 {MT, FS}. We start with a simpleexamination of three sets of position distances: (1) intrasubjectdistances between the MT and FS methods: Dmtfs 5fjPlMT

x� PlFSx jjx 2 Sg; (2) intersubject distances within the MT

data: Dmtmt 5 fjPlMTx

� PlMTyjj x,y 2 S, x=y}; and (3) intersub-

ject distances within the FS data: Dfsfs 5 fjPlFSx � PlFSy jj x,y 2S, x=y}. Similar calculations are applied to the right hippo-campus and the orientation measures. The orientation distanceis measured by the angle between two directions.

Figure 6 shows the distributions of all these distance collec-tions. The MT and FS methods show good agreement on posi-tion measures: The intrasubject distances between the twomethods (Dmtfs, top row) are narrowly distributed and close tozeros, compared with large intersubject variations shown inboth MT data (Dmtmt, middle row) and FS data (Dfsfs, bottomrow). As to orientation, the intrasubject distances between the

TABLE 1.

Performance Comparison of Different Surface Registration Methods

MT data FS data

FOE-PRM FOE-OBJ OUR-ALG FOE-PRM FOE-OBJ OUR-ALG

Global 16.12 6 2.32 2.93 6 0.73 2.84 6 0.76 13.97 6 3.52 3.32 6 0.50 3.08 6 0.50

Local 16.12 6 2.32 1.88 6 0.37 1.82 6 0.34 14.03 6 3.50 2.81 6 0.42 2.76 6 0.41

FOE-PRM: Rotate parameterization to a canonical position using FOEs; FOE-OBJ: One run of object space registration after FOE-PRM; OUR-ALG: Ouralgorithm; Global: Global registration; Local: Local registration.

FIGURE 6. Distributions of intrasubject and intersubject dis-tances on (a) position measures and (b) orientation measures afterglobal registration. In each of (a) and (b), the left column showsthe results for the left hippocampi and the right column for theright ones. Shown in the top row are intrasubject distances

between the MT and FS methods, in the middle row intersubjectdistances within the MT data, and in the bottom row intersubjectdistances within the FS data. [Color figure can be viewed in theonline issue, which is available at www.interscience.wiley.com.]

COMPARISON OF MANUAL AND AUTOMATED SEGMENTATION OF THE HIPPOCAMPUS 593

Hippocampus

two methods (top row) are similar to the intersubject distanceswithin either of the methods (middle and bottom rows). Allthese distances are small, suggesting a consistent similarity inrelative orientations between two hippocampi. For local regis-tration, we did the same experiments, and all the distances onposition or orientation measures became zero. This indicatesthat local registration excludes both global and local translationand rotation effects of each individual hippocampus.

Finally, we examine the shape measures. Since we have estab-lished surface correspondence among SPHARM models, givenany two models, we can calculate a distance map showing themodel difference on each surface location. Similar to the posi-tion and orientation cases, we calculate three sets of distancemaps: (1) intrasubject distances between MT and FS methods,(2) intersubject distances within the MT data, and (3) intersub-ject distances within the FS data. Since it is hard to visualizethe distance distribution for all the surface locations, we onlyshow the mean and standard deviation of these distance mapsin Figure 7. For global registration, compared with the inter-subject distances within a method (MT or FS, refer middle orbottom row), the intrasubject distances between two methods(top row) are relatively small. This indicates some degree ofagreement, but it might be related only to position and orien-

tation factors, since globally registered models contain not onlyshape but also position and orientation information. In localregistration, the position and orientation information isexcluded, and the intrasubject distances between the methods(top row) and the intersubject distances within the method(middle and bottom rows) become at a similar level. To makethe two methods agree more on shape, it appears there is stillroom for improvement. In particular, the tail region shows anotable systematic difference. By a visual inspection of the rawdata (Fig. 1), we notice that the FS results tend to have a fattertail and some noisy spikes on the surface.

DISCUSSION

We have compared FreeSurfer (V4) with a published manualprotocol on the determination of hippocampal boundariesfrom MRI scans, using data from an existing MCI/AD cohort.This comparison is enabled by an improved SPHARM model-ing and registration procedure. We use a global registrationprocess to extract relative position and orientation measures ofthe two hippocampi, and the manual and FreeSurfer methods

FIGURE 7. Statistical maps of intrasubject and intersubjectsurface distances after (a) global registration or (b) local registra-tion. In each of (a) and (b), the left column shows the mean map,and the right column shows the standard deviation map. Shown inthe top row are intrasubject distances between the MT and FSmethods, in the middle row intersubject distances within the MT

data, and in the bottom row intersubject distances within the FSdata. All the mean and standard deviation maps are color-codedand superimposed onto the hippocampal atlas. [Color figure canbe viewed in the online issue, which is available atwww.interscience.wiley.com.]

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show good agreement on these measures. We use a local regis-tration process to account for possible nonrigid-body transfor-mation effects and extract fine-scale surface shape measures,and the two methods show less agreement on these measures.Visual inspection of raw data shows the FreeSurfer results tendto have a fatter tail and some noisy spikes on the surface. Weplan to examine a refined FreeSurfer segmentation strategy andexpect an improved agreement on shape features. Other inter-esting future topics include: (1) Can the results be greatlyimproved by involving minimal human intervention? (2) Arethe discriminative powers to detect disease similar between themanual and FreeSurfer data?

Acknowledgments

The authors thank Nick Schmansky and Bruce Fischl ofHarvard Medical School, and Randy Heiland of Indiana Uni-versity for their help in running the FreeSurfer on IU’ssupercomputers.

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COMPARISON OF MANUAL AND AUTOMATED SEGMENTATION OF THE HIPPOCAMPUS 595

Hippocampus