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COMPARISON OF ATLAS-BASED SEGMENTATION OF SUBCORTICAL STRUCTURES IN MAGNETIC RESONANCE BRAIN IMAGES S. Yousefi1, N. Kehtarnavaz1, A. Gholipour2, K. Gopinath3, R. Briggs3 1

Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX Department of Radiology, Children’s Hospital, Harvard Medical School, Boston, MA 3 Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 2

affine transformation functions with at most 12 degrees of freedom (DOF) for translation, rotation, scaling and shearing [1]. Nonlinear or deformable registration methods involve transformation functions with much higher DOF [2]. Linear registration methods often do not perform well in regions suffering from nonlinear distortions since their use of limited DOF does not provide the flexibility needed to correct for such distortions. On the other hand, deformable or nonrigid registration methods are normally biased by the choice of the template used [12]. Often, a tradeoff is established between the accuracy and complexity of the registration method used. In the widely used MR subcortical segmentation software tool FIRST [3], a captured image is registered to a standard template by using two consecutive affine transformations. The first affine transformation is applied to the entire brain area followed by the second affine transformation applied to the subcortical structures. Heckmann et al. [4] in their atlas-based segmentation method used a combination of rigid and affine transformation followed by a nonrigid transformation applied to the entire brain area. In the atlas-based segmentation method proposed by Chupin et al. [5], the nonrigid registration of the SPM5 [6] tool was utilized. In [7], Aljaber et al. investigated an atlas-based segmentation approach by using a two-step affine and nonrigid registration applied to the entire brain area. In this paper, we have examined the performance of the above types of registration that are deployed in atlas-based segmentation. In addition to the above methods, our previously developed two-step registration method in [10] is considered. Section 2 provides an overview of various registration methods used in atlas-based segmentation. Section 3 provides the framework used to compare these methods for segmentation of subcortical structures. In section 4, the comparison results are presented. Finally, the conclusions are stated in section 5.

ABSTRACT This paper presents an atlas-based segmentation method for subcortical structures in magnetic resonance brain images. This method utilizes our previously introduced two-step registration comprising an affine transformation applied to the entire brain area followed by a local nonrigid transformation applied to subcortical structures. This method is compared to three existing atlas-based segmentation methods by using two objective segmentation indices, namely dice and relative error on volume. The IBSR database is considered for which expert segmented subcortical structures are available. The results obtained show that the proposed atlas-based segmentation method outperforms the existing atlas-based segmentation methods. Index Terms— Magnetic resonance brain images, atlasbased segmentation, brain subcortical structures, medical image registration

1. INTRODUCTION Atlas-based segmentation of magnetic resonance (MR) brain images is widely used for studying brain abnormalities. A brain atlas used for segmentation consists of two parts: a structural MR brain image and its corresponding manual segmentation. Given such an atlas, the segmentation of a query MR image starts by registering the atlas image to the query image and saving the registration transformation. Then, this transformation is applied to the segmented atlas image to yield a segmentation of the query image. The labels get transformed in the same manner. Thus, atlas-based segmentation is very much dependent on the registration method used. Considering that the cortical patterns and substructures in the brain are highly variable, registration plays a key role in the accuracy of atlas-based segmentation. In general, there are two types of registration methods: linear and nonlinear. Linear methods involve

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SSIAI 2010

2 in this paper. Another popular registration method discussed in [4], [7], [9] is also done in two steps, an affine transformation followed by a nonrigid transformation function, both applied to the entire brain area. Let us refer to this registration as type 3 in this paper. In [10], we presented another type of registration consisting of an affine transformation applied to the entire brain area followed by a local nonrigid transformation applied to an area of interest or a masked brain area. Let us refer to this registration as type 4 in this paper.

2. REGISTRATION TYPES IN ATLAS-BASED SEGMENTATION Registration, that is finding a spatial transformation to map the pixels of one image to the homologous pixels of another image, is an integral component of most widely used MR image processing software packages. A source or fixed image f(X) and a target or a moving image m(X) serve as the inputs for the registration process. A spatial transformation, represented by T(X), maps the fixed image space to the target image space. As part of the registration process, a metric, represented by S(f(X),T(m(X)) here, is used to measure the level of alignment of the transformed image and the fixed image. An interpolator is applied to interpolate the target image, represented by I(T(m(X)). Registration is then achieved by formulating and solving an optimization problem indicated here by O(S(f(X))). Figure 1 illustrates the components involved in the registration process. For details of these components, the interested reader is referred to [8].

3. COMPARISON FRAMEWORK This section provides the framework for atlas-based segmentation of subcortical structures. This framework is used to compare our proposed atlas-based segmentation method with the existing atlas-based segmentation methods. Figure 3 shows a block diagram of the comparison framework.

Figure 3. Atlas-based segmentation

As shown in Fig. 3, there is a registration block followed by a label propagation block. It is desired to segment a query image based on an MR atlas image and its manually identified labels. The process starts by registering the query image to the atlas image and saving the registration transformation function. Then, the labels of the atlas image are propagated to the query image based on the saved transformation function. Depending on the four types of registration mentioned in section 2, the labels are propagated accordingly, thus leading to four different atlas-based segmentation outcomes. In the next section, these four different segmentation outcomes are compared to see which one provides the most accurate segmentation results.

Figure 1. Registration components

Let us refer to a one-step registration process as type 1 in this paper. Figure 2 shows a block diagram of this type of registration. This type is used in the initial phase of most atlas-based segmentation approaches.

4. COMPARISON RESULTS 4.1. Database Figure 2. Type1 registration

The database used in this study is the widely used database of Internet Brain Segmentation Repository (IBSR) [11]. The advantage of this database is that expert segmented images are made available along with the original images. We performed two tests. In each test, we selected 7 atlases from the database: one image was used as the reference image and 6 images as the query images. Each test included a different set of images with no overlap. Fig. 4 shows a sample atlas

The other type of registration used for segmentation of subcortical structures is the FIRST tool [3] that is a twostep registration. In the first step, an affine transformation function is applied to the entire brain area and in the second step an affine transformation function is applied only to the subcortical structures. Let us refer to this registration as type

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image from the IBSR database and its corresponding manually identified labels in three different views.

two commonly used indices. The dice overlap index D provides the amount of overlap in the intersection of two datasets and is defined as:

D=

2vol ( A ∩ R) vol ( A) + vol ( R)

(1)

where vol denotes volume and A the segmented image and R the reference image (manually segmented). The optimal value for this index is 1. The relative error index RV provides the difference in volumes of the segmented image and the reference image and is defined as:

RV =

2 | vol ( A) − vol ( R) | vol ( A) + vol ( R )

(2)

The optimal value of this index in percentage is 0. 4.3. Results and discussion

Figure 4. First row from left to right: Axial, Coronal and Sagittal views of a sample atlas from the IBSR database, second row: corresponding manual labels

4.2. Experiments The piecewise affine transformation function with 9 DOF was used in our experiments. We used the “Flirt” tool of the FSL [3] package with the default settings for this purpose. The utilized nonrigid or deformable transformation function is based on a Free Form Deformable (FFD) field and cubic B-spline basis functions. We used the “fnirt” tool of the FSL package using a hierarchical coarse-to-fine setting of 8, 4, 2, and 2. Other settings were left as default. Normalized mutual information (NMI), a widely used measure of registration fidelity, was used for optimization of the cost function for the affine and nonrigid registration. A preprocessing step which is normally done when processing MR images was also applied before carrying out the segmentation comparison. This preprocessing comprised aligning all the images and their manual labels to the Montreal Neurological Institute/International Consortium of Brain Mapping (MNI/ICBM-152) standard template. For type 2 and type 4 registrations, the MNI subcortical structures of this template was used as the mask in the second step. Then, for segmentation comparison, the reference image was registered to the 6 query images (image1 to image6 noted in the tables to follow) in four different ways and its labels were propagated to the query images according to the corresponding transformation function. The segmentation outcomes were compared to the corresponding reference labels (manually segmented images available in the IBSR database) using two segmentation indices: dice (D) and relative error on volume (RV). In [5], several segmentation indices are listed. We selected these

Let us refer to the different atlas-based segmentation outcomes as type 1, 2, 3 and 4 depending on the registration type utilized (type 4 is our proposed atlasbased segmentation method). Figure 5 shows a sample outcome of type 4 segmentation, where an IBSR MRI image was segmented using our proposed atlas-based segmentation method (black area), then the manually segmented image of this IBSR image (white area) was used as the gold standard to compare the accuracy of our method. The black area labels denote the outcome of our atlas-based segmentation (type 4). These labels are overlaid on the reference manual labels of the IBSR image displayed in white for visual comparison purposes. In other words, the white areas exhibit the mismatch areas with the reference manual segmentation. Table 1 shows the comparison of the segmentations in terms of the dice overlap index for the first test. Column 1 lists the dice overlap index when using type 1 atlas-based segmentation as compared to the manually segmented images. In columns 2, 3 and 4, the dice overlap index for type 2, 3 and 4 are listed, respectively. As can be seen from this table, this index is consistently higher for type 4 segmentation. As listed in Table 2, the second test also shows our proposed atlas-based segmentation (type 4) outperforms the other types of atlas-based segmentation.

Figure 5.Atlas labels overlaid on reference labels showing the accuracy of atlas propagated labels when using type 4 registration (black areas represent match, white areas mismatch)

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segmentation indices, it has been shown that the proposed atlas-based segmentation method derived from our previously introduced two-step registration generates the most accurate segmentation outcome for subcortical structures in magnetic resonance brain images.

Test #1 Type 1 Type 2 Type 3 Type 4 IBSR image1 0.75 0.75 0.80 0.82 IBSR image2 0.74 0.73 0.78 0.82 IBSR image3 0.78 0.76 0.82 0.83 IBSR image4 0.81 0.80 0.82 0.83 IBSR image5 0.78 0.81 0.85 0.86 IBSR image6 0.79 0.80 0.84 0.85 Table 1. Comparison of the degree of segmentation accuracy in terms of dice overlap index for test 1 Test #2 Type 1 IBSR image1 0.67 IBSR image2 0.74 IBSR image3 0.76 IBSR image4 0.74 IBSR image5 0.78 IBSR image6 0.72 Table 2. Comparison of the degree dice overlap index for test 2

Type 2 Type 3 0.72 0.80 0.71 0.81 0.77 0.78 0.73 0.79 0.77 0.84 0.72 0.78 of segmentation accuracy in

6. REFERENCES [1] J. Talairach, and P. Tournoux, Co-planar Stereotaxic Atlas of the Human Brain, Thieme: New York, 1988. [2] D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D.J. Hawkes, “Non-rigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imag., vol. 18, pp. 712-721, 1999. [3] http://www.fmrib.ox.ac.uk/fsl/index.html [4] R.A. Heckemann, J.V. Hajnal, P. Aljabar, D. Rueckert, A. Hammers, “Automatic anatomical brain MRI segmentation combining label propagation and decision fusion,” Neuroimage, vol. 33, pp. 115-126, 2006. [5] M. Chupin, A. R. Mukuna-Bantumbakulu, D. Hasboun, E. Bardinet, S. Baillet, S. Kinkingnéhun, L. Lemieux, B. Dubois, and L. Garnerob, “Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease,” Neuroimage,vol. 34, pp. 996-1019, 2007. [6] http://www.fil.ion.ucl.ac.uk/spm/ [7] P. Aljabar, R.A. Heckemann, A. Hammers, J.V. Hajnal, , D. Rueckert,”Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy,” Neuroimage,vol. 46, pp. 726738, 2009. [8] A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, K. Gopinath, “Brain functional localization: A survey of image registration techniques,” IEEE Trans. on Med. Imag., vol. 26, pp. 427-451, 2007. [9] E.M. van Rikxoort, I. Isgum, M. Staring, S. Klein and B. van Ginneken, “Adaptive local multi-atlas segmentation: Application to heart segmentation in chest CT scans,” SPIE Med. Imaging, vol. 6914, pp. 691407-01-691407-06, 2008. [10] S. Yousefi, N. Kehtarnavaz, K. Gopinath, and R. Briggs, “Two-stage registration of substructures in magnetic resonance brain images,” Proceedings of IEEE ICIP Conference, Nov. 2009. [11] http://www.cma.mgh.harvard.edu/ibsr [12] B. Patenaude, Bayesian Statistical Models of Shape and Appearance for Subcortical Brain Segmentation, PhD Dissertation, University of Oxford, 2007.

Type 4 0.80 0.81 0.81 0.81 0.85 0.82 terms of

In Table 3, the comparison of the segmentation methods in terms of the RV index is listed for the first test. The RV index for type 1 atlas-based segmentation appears in column 1. In columns 2, 3 and 4, the RV indices for type 2, 3 and 4 are listed, respectively. Test #1 Type 1 Type 2 Type 3 Type 4 IBSR image1 25 21 18 18 IBSR image2 32 26 19 18 IBSR image3 19 21 12 13 IBSR image4 13 14 14 13 IBSR image5 21 18 14 14 IBSR image6 17 19 16 16 Table 3. Comparison of the degree of segmentation accuracy in terms of relative error on volume index expressed in % for test 1

As can be seen from this table, on average, our proposed type 4 segmentation generates better RV indices in comparison to type 1 and type 2 and nearly the same indices with respect to type 3. A similar outcome is obtained for test 2 shown in Table 4. Test #2 Type 1 Type 2 Type 3 Type 4 IBSR image1 28 24 16 17 IBSR image2 27 29 16 17 IBSR image3 17 16 15 14 IBSR image4 16 16 15 14 IBSR image5 18 18 15 14 IBSR image6 21 20 21 19 Table 4. Comparison of the degree of segmentation accuracy in terms of relative error on volume index expressed in % for test 2

7. ACKNOWLEDGEMENT This study was jointly supported by the UTD Erik Jonsson School of Engineering and Computer Science, and a subcontract from UT Southwestern Medical Center at Dallas, funded by the Department of Veterans Affairs through VA IDIQ contract number VA549-P-0027 awarded and administered by the VA Medical Center, Dallas, TX. The content of this paper does not necessarily reflect the position or the policy of the Veterans Administration or the Federal government, and no official endorsement should be inferred.

5. CONCLUSION This paper has introduced an atlas-based segmentation method for subcortical structures in magnetic resonance brain images and its comparison with the existing atlasbased segmentation methods. By considering two objective

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