pre-printThe degree of white matter (WM) myelination is rather inhomogeneous across the brain. As a consequence, white matter appears differently across the cortical lobes in MR images acquired during early postnatal development. At 1 year old specifically, the gray/white matter contrast of MR images in prefrontal and temporal lobes is limited and thus tissue segmentation results show commonly reduce accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted image to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance inhomogeneity is highly reduced by the age of 24 months. For that purpose, we employ MRI data from a large dataset of longitudinal (12 and 24 month old subjects) MR study of Autism. The IGM creation is based on automatically co-registered images at 12 months, corresponding registered 24 months images, and a final registration of all image to a prior average template. In template space, voxelwise correspondence is thus achieved and the IGM is computed as the coefficient of a voxelwise linear regression model between corresponding intensities at 1-year and 2-years. The proposed IGM shows low regression values of 1-10% in GM and CSF regions, as well as in WM regions at advanced stage of myelination at 1-year. However, in the prefrontal and temporal lobe we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes due to myelination.The IGM is applied to cross-sectional MRI datasets of 1-year old subjects via registration, correction and tissue segmentation of the corrected dataset. We validated our approach in a small study of images with known, manual "ground truth" segmentations. We furthermore present an EM-like optimization of adapting existing non-optimal prior atlas probability maps to fit known expert rater segmentations

Gerig, Guido

Kim, Sun Hyung

English

The University of Utah: J. Willard Marriott Digital Library

SPATIAL INTENSITY PRIOR CORRECTION FOR TISSUE SEGMENTATION IN THEDEVELOPING HUMAN BRAINSun Hyung Kima, Vladimir Fonovb,Joe Pivenc, John Gilmorea, The IBIS Network, Clement Vacheta,Guido Gerige, D. Louis Collinsb, Martin Stynera,daDepartment of Psychiatry, University of North Carolina at Chapel Hill, USAbMcConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, CanadacCarolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, USAdDepartment of Computer Science, University of North Carolina at Chapel Hill, USAeScientiﬁc Computing and Imaging Institute, University of Utah, Salt Lake City, USAABSTRACTThe degree of white matter (WM) myelination is rather inho-mogeneous across the brain. As a consequence, white mat-ter appears differently across the cortical lobes in MR imagesacquired during early postnatal development. At 1 year oldspeciﬁcally, the gray/white matter contrast of MR images inprefrontal and temporal lobes is limited and thus tissue seg-mentation results show commonly reduce accuracy in theselobes. In this novel work, we propose the use of spatial in-tensity growth maps (IGM) for T1 and T2 weighted image tocompensate for local appearance inhomogeneity. The IGMcaptures expected intensity changes from 1 to 2 years of age,as appearance inhomogeneity is highly reduced by the age of24 months. For that purpose, we employ MRI data from alarge dataset of longitudinal (12 and 24 month old subjects)MR study of Autism. The IGM creation is based on auto-matically co-registered images at 12 months, correspondingregistered 24 months images, and a ﬁnal registration of allimage to a prior average template. In template space, vox-elwise correspondence is thus achieved and the IGM is com-puted as the coefﬁcient of a voxelwise linear regression modelbetween corresponding intensities at 1-year and 2-years. Theproposed IGM shows low regression values of 1-10% in GMand CSF regions, as well as in WM regions at advanced stageof myelination at 1-year. However, in the prefrontal and tem-poral lobe we observed regression values of 20-25%, indicat-ing that the IGM appropriately captures the expected largeThe IBIS (Infant Brain Imaging Study) Network is an NIH fundedAutism Center of Excellence (HDO55741) and consists of a consortium of7 Universities in the U.S. and Canada. Clinical Sites: University of NorthCarolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; Universityof Washington: S. Dager, A. Estes; Washington University: K. Botteron, R.McKinstry, J. Contstantino, L. Flake ; Childrens Hospital of Philadelphia: R.Schultz, S. Paterson; University of Alberta: L. Zwaigenbaum. Data Coor-dinating Center: Montreal Neurological Institute: A. Evans, L. Collins, B.Pike, R. Aleong, S. Das. Image Processing Core: University of Utah: G.Gerig; University of North Carolina: M. Styner. Statistical Analysis Core:University of North Carolina: H. Gu. Genetics Analysis Core: University ofNorth Carolina: P. Sullivan, F. Wright.intensity change in these lobes due to myelination.The IGMis applied to cross-sectional MRI datasets of 1-year old sub-jects via registration, correction and tissue segmentation ofthe corrected dataset. We validated our approach in a smallstudy of images with known, manual ”ground truth” segmen-tations. We furthermore present an EM-like optimization ofadapting existing non-optimal prior atlas probability maps toﬁt known expert rater segmentations.Index Terms— MRI, tissue segmentation, expectationmaximization (EM) algorithm, classiﬁcation1. INTRODUCTIONBrain tissue segmentation is a fundamental analysis step toanatomical studies of neurodevelopment. Many methods havebeen proposed that segment MR images into tissue classes ofwhite matter (WM), gray matter (GM) and cerebrospinal ﬂuid(CSF). The most widely employed approaches are expecta-tion maximization (EM), artiﬁcial neural network and fuzzyclassiﬁcation based algorithms [1, 2, 3]. These methods workwell on images from subjects older than 2 years of age, as thewhite matter of the brain has matured enough in order to ap-pear mostly homogenous across the brain. But WM in earlypostnatal stage undergoes myelination that strongly affectsMR appearance. The intensity of not-fully-myelinated WMoften appear similar to GM intensity. The progress of myeli-nation in WM is well known be inhomogeneous across thebrain by following a pattern of posterior-to-anterior lobes andsuperior to inferior progression. At 1 year old, the inferiorfrontal lobes and temporal poles show consequently a reducedWM/GM contrast as compared to other lobes. Not surpris-ingly, standard tissues segmentation methods, which assumehomogeneous within-class appearance across the image, pro-duce incorrect results within the prefrontal and temporallobes. Commonly, white matter is undersegmented in theselobes. To address this, the addition of a mixed WM/GM class86*RYHUQPHQW:RUN1RW3URWHFWHGE\86&RS\ULJKW ,6%,or the use of regional/lobar atlases were proposed [4], thoughwith limited success unless paired longitudinal datasets exist.The purpose of this study is to develop a novel method forbrain tissue segmentation of cross-sectional 1-year old MRIdatasets using a novel spatial intensity growth map (IGM).Our IGM captures expected intensity changes from 1 to 2years of age, as appearance inhomogeneity is highly reducedby the age of 24 months. The IGM is applied to MRI im-ages by deformable registration and subsequent intensity cor-rection. The modiﬁed image is then segmented with an EMbased tissue segmentation method. The proposed method isthen evaluated on 3 T1 weigthed images of 1 year old subjectswith manual ”ground truth” segmentation.2. METHODS2.1. DataThe study population consists of fourteen subjects with longi-tudinal T1(160 slices with TR=2400ms, TE=3.16ms, ﬂip an-gle=8, ﬁeld of view 224 × 256) and T2 weighted (160 sliceswith TR=3200ms, TE=499ms, ﬂip angle=120, ﬁeld of view256× 256) MR scans at 12 and 24 months. The subject scansare a selection from scans within the IBIS (Infant Brain Imag-ing Study) network1 acquired at 4 different sites. The reasonthat we are randomly selected from the autism and healthydatasets is to avoid being biased segmentation results towardthe speciﬁc group. All datasets were acquired on 3T SiemensTim Trio scanners at the same resolution of 1 × 1 × 1mm3.Three additional subjects from the same study were selectedfor the evaluation study. Using ITK-SNAP2, trained expert re-searchers determined manual segmentations of WM, GM andCSF of the full T1 image.2.2. PreprocessingAn overview of processing is shown in Fig.1. All images wereﬁrst corrected for intensity nonuniformity using N3 [5] result-ing from inhomogeneities in the magnetic ﬁeld. Then we ex-tracted the skull using FSL-BET (Brain Extraction Tool)3 forall subjects [6]. All T2 weighted images were rigidly regis-tered to the corresponding T1 weighted images. Then, the T1and T2 images at two years of age were mapped into theircorresponding intrasubject 1-year old dataset by rigid regis-tration followed by cross-correlation, thin-plate spline baseddeformable registration [7]. Finally, all T1 MRIs of 1-yearsubjects were then registered into a prior, common, averagetemplate coordinate space by 9 parameter (similarity trans-form) registration followed by the same deformable registra-tion. The concatenated registration transforms were appliedto all the other images, such that all images (T1 weighted and1http://www.ibis-network.org2http://www.itksnap.org3http://www.fmrib.ox.ac.uk/fsl/bet2T1 and T2 Raw MRI Nonuniformity correction (N3) Stereotaxic Space Skull Strip (BET) 1Yr Atlas T1 Warping 4Yr Atlas & Tissue Probability Aligned T1 Linear Regress Intensity Growth Map Registration  (9 parameters) Initial 1Yr Prior Probability T2 Warping EM Segmentation Partial Volume Estimation Adaptive Tissue Probability for 1Yr Error < 0.01 Error > 0.01 Applied IGM-T1 &T2 MRI Decision  Tissue ProbabilityFig. 1: Overview of IGM calculation and generation of theoptimized tissue probabilities using an iterative EM process.T2 weighted at both 1 and 2 years) are mapped into a com-mon template space. The unbiased, age-appropriate (1-year)atlas template was computed via joint deformable registrationthat simultaneously minimizes the difference of intensity andtransformation [8] from 66 training 1-year old datasets withinthe IBIS study. Initial probabilistic spatial priors for all tis-sue classes were determined by co-registering and adaptingan existing 4-year old template (see 2.5).2.3. Intensity growth map (IGM) generationBased on the voxelwise correspondence in the average tem-plate space established in the preprocessing, we compute theIGM as the local coefﬁcient map (αi) from a reduced linearregression model (Yi = αi ·Xi ) between the local intensityat 2-year (Yi ) and 1-year (Xi) old data in the each voxel (i).Whereas a monoexponential or second order model would beneeded for regression across a longer period or when incorpo-rating multiple time points [9, 10], the selected linear model isa reasonable choice when we focus on a 1-year period from 12to 24 months of age with only two known timepoints. Whilethe common linear regression model also includes a constantterm, we did not consider this constant term, as we assumethat the background has the same zero mean intensity at bothages. The resulting IGM map (see Fig. 2) will show high val-ues of areas of high intensity change and low values for thoseregions of moderate intensity change from 12 to 24 months.2.4. Enhanced IGM based tissue segmentationLike most atlas based segmentation approaches, our EM seg-mentation registers a prior atlas template to a new subject’s T1or T2 datasets and maps the atlas tissue priors to the subjectspace. In our approach, we also map the IGM map into thesubject image space. The T1 and T2 IGMs are then convo-luted with each T1 and T2 weighted image to yield intensitycorrected T1 and T2 images as input for an EM based tissuesegmentation [11]. Next to the posterior WM, GM and CSFprobability maps, our EM segmentation approach also pro-vides a partial volume estimation map (PVE) for each tissuetype. Using a thinning based 1D-skeleton of the WM-PVEmap binarized at 10%, we further enhance the posterior WMprobability setting the WM-PVE-skeleton to 1, as the WMoften is thin and more variable in the temporal pole and pos-terior occipital lobes. This last step does little to the overallvolume (less than 1% change), but provides considerable en-hancement to any potential cortical thickness analysis follow-ing the tissue segmentation.2.5. Prior optimizationThe result of our atlas based brain tissue segmentation isstrongly dependent on the prior tissue probability maps de-ﬁned in the atlas template space. In our application, theinitial tissue class priors in the atlas space were determinedby deformable registration of an existing 4-year old atlaswith known probability priors into the 1-year old atlas. Asthese mapped probabilistic tissue maps may not be fully ap-propriate for our 1 year atlas, and we employed an EM-likeframework to optimize these maps. The tissue segmentationmethod (described above) was thereby treated as a black box.As a ﬁrst step, using the propagated 4-year old probabilitypriors, we computed the segmentation of the 14 subjectsalready employed in the IGM computation. The resultingposterior maps were mapped back in the atlas space, wherethey were averaged to represent updated prior probabilitymaps. Then, for all subsequent iterations, we computed thesegmentation of three ”training” subjects with known man-ual expert segmentation and compared these to the currentiteration’s hard segmentation maps. Difference maps werecomputed for each tissue class (0 = correct segmentation;+1= false positive; -1 = false negative), averaged across all threetraining subjects in the atlas space, and additively applied tothe prior probability maps. The optimization iterated untilthe prior probabilities converged (less than 1% cumulativechange in probability across the WM, GM and CSF priors.Convergence was established after 4 iterations.3. RESULTS3.1. Intensity growth mapThe computed T1 IGM reﬂects the expected maturation re-lated MR intensity changes between 1-year and 2-year old(see Fig.2). GM and CSF, which are not related to the myeli-nation process, revealed relatively low change coefﬁcients αibetween 1.0 and 1.1 in the T1 image. In WM regions that al-ready underwent considerable myelination, we measured sim-ilar coefﬁcient values to those in GM and CSF regions. How-ever, in those WM areas that are known to exhibit a compar-T1-IGM T2-IGM 1.3 1.0 1.0 0.7 Fig. 2: IGM shows regression coefﬁcients of about 25% inthe prefrontal and temporal lobe regions (ITK-SNAP visual-ization).atively lower stage of myelination at 1-years of, we observed20-30% intensity difference between 1-year and 2-year olds(i.e. coefﬁcients(αi) around 1.25). The IGM coefﬁcients forthe T2 weighted images provided the same interpretations al-though it appears inverse when compared to the T1 weightedIGM. In both T1 and T2 IGM, the superior frontal lobe, infe-rior temporal lobe and temporal pole changed the most. IGMcorrected MR images of 1 year old subjects appear visuallywith the appearance of a 2 year old’s MR image.3.2. Adaptive one-year-old tissue probability atlas andexperimental resultsFig. 3 displays how under-estimations occurred in the inferiortemporal lobe and frontal lobe using a conventional EM seg-mentations method on the uncorrected 1-year old data withknown ground truth. In addition over-estimation also occuredin the superior frontal lobe due to the low contrast in thatregion. However, when we applied our proposed IGM-EMbased method, we obtained more accurate segmentations ofthe WM and GM, especially in the low contrast areas of theinferior temporal and superior frontal lobe. We validated theaccuracy of the IGM-EM segmentation methods against thethree datasets with known manual segmentations. Since thosesame datasets were also used in the optimization of the prioratlas probability maps (see 2.5, we employed a leave-one-outscheme for all parts of the validation. Thus, for each valida-tion dataset the atlas probability maps were optimized onlyover the other 2 datasets. A clear improvement in the inferiorfrontal, temporal and posterior occipital lobe is visible. Theseregions are at a lower stage of myelination at 1 year of age.We further favorably compared the performance of our pro-posed IGM-EM versus a conventional EM method and FSL’sFIRST method (Fig.4) using the Tanimoto volumetric overlaperror against the manual segmentation.Fig. 3: Visual comparison of segmentation. The WMwas under-estimated in prefrontal and inferior temporal lobe(white dot circles) using the conventional EM algorithm ascompared to the IGM-EM results and the expert ground truth.4. CONCLUSIONThe quantitative analyses of postnatal development usingbrain tissue volume and cortical thickness measurements,which are main components of most traditional anatomicalMRI studies, are based on accurate brain tissue segmenta-tion. Here, we propose a correction and tissue segmentationmethodology that allows a standard brain tissue segmentationmethod to handle low myelination areas in 1-year old brainMRIs. The method is based mainly on a trained longitudinalmodel of MR intensity change from 1-year to 2-year old. Wefurthermore presented a EM-based optimization of adaptingexisting non-optimal prior probability maps to ﬁt know expertrater segmentations.References[1] JC Fu, CC Chen, JW Chai, STC Wong, and IC Li, “Imagesegmentation by EM-based adaptive pulse coupled neural net-works in brain magnetic resonance imaging,” ComputerizedMedical Imaging and Graphics, vol. 34, no. 4, pp. 308–320,2010.[2] T.B. Dyrby, E. Rostrup, W.F.C. Baare´, E.C.W. van Straaten,F. Barkhof, H. Vrenken, S. Ropele, R. Schmidt, T. Erkinjuntti,L.O. 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Thompson, C.M. Leonard, S.E. Welcome,E. Kan, and A.W. Toga, “Longitudinal mapping of corticalthickness and brain growth in normal children,” Journal ofNeuroscience, vol. 24, no. 38, pp. 8223, 2004.[11] J.A. Bilmes, “A gentle tutorial of the EM algorithm and itsapplication to parameter estimation for Gaussian mixture andhidden Markov models,” International Computer Science In-stitute, vol. 4, 1998.

Spatial intensity prior correction for tissue segmentation in the developing human brain

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Spatial intensity prior correction for tissue segmentation in the developing human brain

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