A new rapid landmark-based regional MRI segmentation method of the brain

Transcription

1 Journal of the Neurological Sciences 194 (2002) A new rapid landmark-based regional MRI segmentation method of the brain A.L.W. Bokde a, S.J. Teipel a, *, Y. Zebuhr a, G. Leinsinger b, L. Gootjes a,c, R. Schwarz a, K. Buerger a, P. Scheltens d, H.-J. Moeller a, H. Hampel a, ** a Dementia and Neuroimaging Section, Department of Psychiatry, Ludwig-Maximilian University, Nussbaumstr. 7, Munich, Germany b Department of Radiology, Ludwig-Maximilian University, Munich, Germany c Department of Clinical Neuropsychology, Free University, Amsterdam, Netherlands d Department of Neurology, Free University Hospital, Amsterdam, Netherlands Received 2 July 2001; received in revised form 2 November 2001; accepted 6 November 2001 Abstract Background: Neurodegenerative and cerebrovascular diseases show a distinct distribution of regional atrophy and subcortical lesions. Objective: To develop an easily applicable landmark-based method for segmentation of the brain into the four cerebral lobes from MRI images. Method: The segmentation method relies on a combination of anatomical landmarks and geometrical definitions. It is applied on the surface reconstruction of the MRI volume. The internal borders between the lobes are defined on the axial slices of the brain. The reliability of this method was determined from MRI scans of 10 subjects. To illustrate the use of the method, it was applied to MRI scans of an independent group of 10 healthy elderly subjects and 10 patients with vascular dementia to determine the regional distribution of white matter hyperintensities (WMH). Results: The intra-rater relative error (and intra-class correlation coefficient) of the lobe segmentation ranged from 1.6% to 6.9% (from 0.91 to 0.99). The inter-rater relative error (and intra-class correlation coefficient) ranged from 1.4% to 5.2% (from 0.96 to 0.99). Density of WMH was significantly higher in all four lobes in VD patients compared to controls ( p < 0.05). Within each group, WMH density was significantly higher in frontal and parietal than in temporal and occipital lobes ( p < 0.05). Conclusion: This landmark based method can accommodate age and disease-related changes in brain morphology. It may be particularly useful for the study of neurodegenerative and cerebrovascular disease and for the validation of template-based automated techniques. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Brain lobes; Regional segmentation; Anatomical landmark; White matter hyperintensities 1. Introduction Neuropathological studies suggest a differential susceptibility of cerebral cortical regions for different neurodegenerative processes [1,2] and cerebro-vascular disease [3,4]. A major challenge in clinical research on neurodegenerative or cerebrovascular disorders of the brain is the reliable identification of regional pattern of disease-related cerebral atrophy and the regional distribution of vascular cerebral lesions. * Corresponding authors. Tel.: ; fax: ** Co-corresponding author. addresses: stt@psy.med.uni-muenchen.de (S.J. Teipel), hampel@psy.med.uni-muenchen.de (H. Hampel). To show the differential involvement in-vivo, two basic methodological approaches exist to segment cerebral regions (or calculate lobar volumes) on MRI scans: (a) an automated method relying on template and (b) a manual method based upon landmarks. There have been several reports on automated methods [5 8] for quantification of the lobe volumes by first normalizing the magnetic resonance images to the Talairach and Tournoux template [9] and segmenting the brain regions based on the template. The template defines every pixel as a member of a specific region and that segmentation is then transferred to the MRI scan of interest. The advantage of automated methods is speed, reliability, and ease of use by the operator. The disadvantage is that it does not take into account differences in shape and variability of the cortex, which are more pronounced in patient populations. In addition, the normalization process will inevitably cause X/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S X(01)

2 36 A.L.W. Bokde et al. / Journal of the Neurological Sciences 194 (2002) an averaging process to take place and individual differences may be lost, which can hide differences in structure between normal populations and patient populations. There are several reports on manual methods for tracing a single or two lobes of the brain, typically the temporal and/or frontal lobe, on a slice by slice basis (see for example Refs. [10 13]) or on a reconstructed 3-D model of the cortex [14]. There is a report on the regional segmentation of the five lobes in the cortex [15]. These methods trace each individual region and segmentation methods are used to delineate brain from CSF. The main advantage of these methods is that differences in shape and variability can be accounted for in application to normal subjects and patient populations. The disadvantages are that it can be time consuming, landmarks can be difficult to detect, and the reliability between and within raters over time has to be confirmed. The objective of this work is the development of a rapid and easy to use landmark-based technique for regional segmentation of the brain into different lobes. The segmentation is manual, with the traces done with reference to prominent sulcal points and drawing well-defined lines when no sulci and/or gyri reference landmarks are available. We will demonstrate the usefulness of the technique with its application to regional measures of white matter hyperintensities (WMH) in a group of patients with vascular dementia and an age-matched healthy control group. 2. Materials and methods 2.1. Subjects The technique was evaluated in a total of 10 subjects that comprised five normal healthy subjects, three patients with clinical probable Alzheimer s disease, and one patient each with clinical probable vascular dementia and fronto-temporal degeneration. The technique was applied to a vascular dementia patient group of 10 patients with average age of 67.7 yearsf7.8 (averagefs.d.) and an age matched control group (age = 69.0 yearsf5.6) of 10 subjects. Alzheimer s disease was diagnosed according to NINCDS-ADRDA criteria [16], vascular dementia was diagnosed according to NINDS-AIREN criteria [17] and fronto-temporal degeneration was diagnosed according to criteria of Neary et al. [18]. All subjects (and/or their legal care-givers) gave informed written consent to participate in this project, which was approved by the Ethics Committee of Ludwig Maximilian University Medical Faculty Magnetic resonance imaging The lobe segmentation was done on images obtained with an MPRAGE sequence (TR = 11.6 ms, TE = 4.9 ms, TI = 300 ms, TD = 0, flip angle = 12, slab thickness = 180 mm), sagittally oriented volumes with 128 slices, a slice thickness of 1.2 mm, pixels per slice, and in-plane pixel size of 1.0 mm. The scanner used was a 1.5 T Siemens Magnetom Vision MRI scanner (Siemens, Erlangen, Germany). Some volume sequences had in-plane pixels that were reduced to pixels per slice before processing. To obtain the measures of white mater hyperintensities (WMH), a fast FLAIR (TR = 9000 ms, TE = 110 ms, TI = 2500 ms, echo train length = 7) sequence was used, with each volume having 20 slices, a slice thickness of 6 mm, a pixel size of mm, and each slice had pixels Description of segmentation technique The initial step of this technique was to remove all nonbrain tissue and cerebro-spinal fluid (CSF) regions using a semi-automated threshold technique. This segmentation technique consisted of a histogram of the volume of data and picking two thresholds between which the values corresponded mainly to tissue outside of the brain. Then a seed was placed in the skull area of the image and region growing technique was applied, with the growth limited to pixel values within the range defined by the two thresholds. As the final step, the mask produced in the previous step was edited so that no brain tissue was deleted and all nonbrain tissue was removed. Next, the brain images were re-oriented, if necessary, such that in the coronal and axial views the inter-hemispheric fissure was vertical on the screen. The vertical alignment was important because the regional segmentation depends on a complete lateral view of each hemisphere when constructing the surface projection. We used the atlas by Michio et al. [19] for sulcal anatomical information. The technique was implemented with Analyze AVW (Mayo Foundation, Rochester, MN, USA). The steps taken to implement this technique will now be described. First, orient brain images in the sagittal orientation, and go to the slice which shows the biggest part of the falx in the left hemisphere. Then go to the first slice in which the parieto-occipital sulcus is clearly visible. Mark the location (point B in Fig. 1) of the sulcus on the posterior side of the brain (we used lines outside the brain to denote the location of sulcus). Brain tissue below the parietooccipital sulcus is the medial surface of the occipital lobe (with the exception of the cerebellum). Trace the occipital lobe on this slice. Based on our experience, the first slice on which the parieto-occipital sulcus is clearly visible is between 5 to 8 mm from the inter-hemispheric fissure. Redo the process for the right hemisphere. Reconstruct a 3D-model of the brain (surface projection), which will show the lines indicating the location of the parieto-occipital sulcus. We always start with the left hemisphere. Identify the central sulcus by the following procedure: look up the three frontal gyri, which run in the anterior posterior direction. At the posterior end of these gyri, the first gyrus which runs perpendicular to them is the

3 A.L.W. Bokde et al. / Journal of the Neurological Sciences 194 (2002) Fig. 1. Medial surface showing line marking of the parieto-occipital sulcus in the posterior region of the brain. Point B is the posterior end of the parieto-occipital sulcus. The occipital lobe has been segmented from the rest of the brain. precentral gyrus. The central sulcus is the posterior border of the precentral gyrus. Draw a trace along the central sulcus. This trace demarcates the border between the frontal and parietal lobes. The trace should extend from the interhemispheric fissure to the sylvian fissure. Because in most cases the central sulcus and sylvian fissure do not intersect, the trace extends with a straight line from the lower end of the central sulcus to the sylvian fissure. Next draw a trace along the sylvian fissure anteriorly and include the entire frontal cortex (see Fig. 2). Draw another trace around the rest of the cortex that includes the parietal, occipital and temporal lobes, but exclude the cerebellum and brain stem. The cerebellum and brain stem can be edited from the images at a later stage. Draw a trace along the sylvian fissure (from anterior to posterior direction) and terminate the trace on the beginning of the ascending terminal ramus of the sylvian fissure. The angular gyrus is located around the ascending terminal gyrus. The end of the trace is point A in Fig. 2. Draw a straight line from the parietoccipital sulcus (indicated in the first step, point B) to point A. At the center of this line, point C in Fig. 1, draw a line perpendicular to the lower side of the brain-point H in Fig. 1. Point D along this line is where it intersects the tissue non-tissue border of the brain. The mid-point along line CD is denoted point X. Draw a straight line from X to A. Next draw a straight line from point B through point X and continue the line until outside of the brain tissue image. The preoccipital notch, a landmark for the border of the parietal and temporal lobes, was not visible in any of the subjects that we analyzed even with the high resolution images that we obtained. Thus the artificial lines that are described above were developed to delineate the borders between the temporal, occipital and parietal lobes. At this point, we have defined all four lobes on the surface of the brain. The frontal lobe is the region indicated with F in Fig. 2, the parietal lobe is indicated with P, the occipital lobe is an O and the temporal lobe is indicated with a T. One can see that the frontal lobe is one area in our tracing, the parietal lobe three areas, the occipital lobe two areas and the temporal lobe one area. The surface of the lobes are now painted with different colors so that when we view individual slices the lobe segmentation will be visible along the cortex surface. The next step is to define the lines separating the lobes in the interior volume of the brain. The brain images are now re-oriented to the axial configuration with the surface of the brain in different colors depending upon region (see Fig. 3 for an example). To separate the frontal and parietal lobes, on every slice draw a perpendicular line from the bottom of the central sulcus to the inter-hemispheric fissure. In the brains in which the inferior end of the central sulcus does not reach the sylvian fissure, the perpendicular line will go from the border between the two lobes to the interhemispheric fissure. Between occipital and parietal lobes draw the shortest straight line from the defined border on the brain surface to the ventricle. The internal border between the occipital and temporal lobes, and between the parietal and temporal lobes, is a straight perpendicular line from the border on the brain surface to the interhemispheric fissure. The cerebellum is well defined and no processing was done on this structure. Fig. 2. Tracings of the regional segmentation of the left hemisphere. The regions are denoted by F frontal cortex, T temporal cortex, P parietal cortex and O occipital cortex. The other letters (A, B, C, D, E, H, X) denote the intersections of lines or reference points that are described in text. The brain image has been filtered to darken it so that the white lines and letters can be better seen.

4 38 A.L.W. Bokde et al. / Journal of the Neurological Sciences 194 (2002) divided by the average of both measures) to assess the extent of difference between measures. Additionally, we calculated the intra-class correlation coefficient to assess reliability between and within raters. The ICC does not only take into account correlations between measures, but is also sensitive to systematic differences in absolute values of measures between ratings [21]. A linear model was used to test for the effect of diagnosis on the distribution of WMH. The variables in the model were the WMH density on the temporal, occipital, parietal and frontal lobes and diagnosis. To test for differences in WMH density in the four regions between groups, we used the Mann Whitney test. 4. Results Fig. 3. Transfer of the regional segmentation to the FLAIR image, with the different colors (shown in grey only) on the surface of the brain indicating the different lobes of the brain. External lines to the brain image have been added so that different regions can be clearly distinguished. The regions outlined in the white matter region are the white matter hyperintensities. All segmentation measures were done by two independent investigators blinded to clinical information from the 10 subjects. Additionally, segmentation from the 10 subjects were repeated by one investigator. For the second measurement the 10 subjects were blinded a second time and randomly mixed with broader set of 42 scans which were measured for the first time. With the above definition of the regions, the structural volume was registered to the FLAIR volume with AIR 3.08 using rigid body registration [20]. The FLAIR image was overlaid over the co-registered segmented MRI. In the FLAIR volumes, the WMH were measured using a semiautomated thresholding technique (see Fig. 3). Areas of WMH were divided by lobe volume to obtain a value for the density of WMH for each lobe. 3. WMH measures were done by one investigator blinded to clinical information 3.1. Statistics To assess inter-rater reliability, scans from the 10 subjects were segmented by two independent investigators; for assessment of intra-rater-reliability, scans were measured twice by one investigator blinded to clinical diagnosis. We used the relative error (positive difference between measures In the first group of data, comprised of healthy controls and various dementia groups, the measured surface areas between independent raters and within one rater of the four different lobes is displayed in Table 1. The relative error and the intra-class correlation coefficients are displayed in Tables 2 and 3, respectively. In the pilot data of the vascular dementia group and the age-matched healthy controls (the quantitative data shown in Table 4), using a linear model with the lobe region and diagnosis as independent variables, we found that both variables contributed significantly to the explanatory power of the model ( p < 0.05). The difference of WMH density between groups, using the Mann Whitney test, was significant ( p < 0.05) in all four regions with higher densities in the vascular dementia patients. Within each group, the WMH density was significantly greater in the frontal and parietal lobes compared to the Table 1 Surface areas of the different lobes measured by the two independent raters and the two measurements made by the single rater Measured surface areas of the two raters Rater 1(a) Rater 1(b) Rater 2 Frontal Right 4842F F F652 Left 4642F F F673 Temporal Right 3173F F F492 Left 2958F F F319 Parietal Right 3218F F F336 Left 3150F F F333 Occipital Right 1398F F F219 Left 1566F F F268 Values are meanf1 SD (mm 2 ).

5 A.L.W. Bokde et al. / Journal of the Neurological Sciences 194 (2002) Table 2 Relative error of surface areas of the different lobes between two independent raters and within a single rater Inter-rater Intra-rater temporal and occipital lobes (using the Wilcoxon test, p < 0.05). 5. Discussion Relative errorfsd (%) Relative errorfsd (%) Frontal Right 1.4F F1.3 Left 1.5F F1.3 Temporal Right 3.5F F2.4 Left 3.1F F1.7 Parietal Right 1.8F F1.3 Left 2.2F F2.6 Occipital Right 5.2F F5.1 Left 2.2F F4.1 In the present study we evaluated a newly developed method for regional segmentation of brain lobar volumes from structural MRI based on anatomical landmarks and well-defined lines. To investigate the potential use of this technique on regional changes in different patient populations, we applied it to a small group of vascular dementia patients and age-matched healthy controls. The intra-class correlation coefficient is highest for the frontal lobe. The correlation coefficients are above 0.96 for Table 3 Intra-class correlation coefficient of surface areas of the different lobes between two independent raters and within a single rater Inter-rater Intra-rater Intra-class correlation coefficient Frontal Right Left Temporal Right Left Parietal Right Left Occipital Right Left Intra-class correlation coefficient Table 4 Regional white matter hyperintensity densities of the vascular dementia patient group and the healthy control group Healthy controls (meanfsd) (10 4 mm 1 ) Vascular dementia (meanfsd) (10 4 mm 1 ) Frontal 3.53F F61.26 Temporal 1.02F F2.73 Parietal 4.23F F55.58 Occipital 0.82F F12.46 all regions except the right occipital region that has a value of The relative error follows the same pattern as the intra-class correlation coefficient with the lowest values for the frontal lobes and highest values for the occipital lobe. The intra-class correlation coefficient in the other regions are not as high as the frontal lobe (and the relative error is not as low) because the segmentation of these regions is through a combination of anatomical landmarks and lines drawn with respect to anatomical landmarks and to each other. The frontal lobe segmentation is done with reference to anatomical landmarks only. The results are consistent within raters and between raters. Comparing our reliability results to those obtained by Aylward et al. [14] on the frontal lobe, both studies had an intra-class correlation coefficient of 0.99 both within and between raters for each of the frontal lobes. Aylward et al. [14] obtained a mean relative error between two raters of 1.2% whereas our mean inter-rater relative error was 1.6% and 1.9% for the right and left frontal lobes, respectively. Both studies had 10 subjects for validation but Aylward et al. s [14] study only had healthy subjects, whereas we had a group composed of healthy subjects and neuro-degenerative disease patients. The reliability of our technique, for the same region, is similar to Aylward et al. s [14] but with a more heterogeneous group. In Fukui and Kertesz [15], the intra- and inter-rater correlation coefficient calculated over all regions, in five subjects, are 0.98 and 0.94, respectively. In our method, when we calculated the intra- and inter-rater correlation coefficients analogue to this study [15], we obtained 0.99 for both coefficients. The high reliability values obtained indicate that the area measures are consistent across the various different groups that we employed. In addition, it can be applied when there are large changes in shape and asymmetries between the lobes without altering the robustness of the method. Changes in sulci and gyri variations, such as may exist in patient populations, will not negatively impact the robustness of the method. It can also serve as a technique for establishing baseline measures on which automatic techniques may be compared to. The regional segmentation can be accomplished in 15 to 20 min per stripped brain, which is obtained after two to five brains of training. This performance is based on the experience of the two raters, one a psychiatrist with 5 years experience in MRI research and the other a dental school student (the intra-rater reliability

6 40 A.L.W. Bokde et al. / Journal of the Neurological Sciences 194 (2002) values are from this rater). We believe that the time needed to segment the brain, as well as obtain the high correlations that we do, is possible with raters who are not experts in neuro-anatomy but have been trained to recognize the specific landmarks specified here. Using the technique, we found greater density of WMH in the frontal and parietal lobes compared to the occipital and temporal lobes both in VD patients and in healthy controls. This is consistent with neuropathological evidence for predominant frontal lobe involvement of cerebral white matter in vascular disease [3] and the architecture of the cerebral vasculature with long penetrating arteries crossing the frontal lobe white matter. The difference between VD patients and healthy controls agrees with previous findings based on semi-quantitative rating scales that showed significantly greater WMH load in VD than in healthy aging [22]. These findings illustrate the potential use of the method for questions relating to the regional distribution of subcortical lesions such as in multiple sclerosis and vascular disease. In conclusion, this method is a powerful tool for the regional segmentation of structural MRI volumes. A method to determine the regional distribution of lobar atrophy and subcortical lesions in-vivo is of high clinical value, because it allows to increase diagnostic accuracy in neurodegenerative and cerebrovascular disorders and to follow the longitudinal course of regional cerebral changes over time. Acknowledgements Part of the presented material originates from the doctoral thesis of Y. Zebuhr (Ludwig-Maximilian University, Munich, Germany; in preparation). Part of this work was supported by a grant of Eisai (Frankfurt) and Pfizer (Karlsruhe), Germany to H.H. and S.J.T. and by a grant of the Medical Faculty of the Ludwig-Maximilian University, Munich, Germany to S.J.T. References [1] Braak H, Griffing K, Braak E. Neuroanatomy of Alzheimer s disease. Alzheimer s Res 1997;3: [2] Bergmann M, Kuchelmeister K, Schmid KW, Kretzschmar HA, Schroder R. Different variants of frontotemporal dementia: a neuropathological and immunohistochemical study. Acta Neuropathol 1996;92: [3] Yamanouchi H, Sugiura S, Tomonaga M. Decrease in nerve fibres in cerebral white matter in progressive subcortical vascular encephalopythy of Binswanger type. J Neurol 1989;236: [4] Erkinjuntti T, Benavente O, Eliasziw M, Munoz DG, Sulkava R, Haltia M, et al. Diffuse vacuolization (spongiosis) and arteriolosclerosis in the frontal white matter occurs in vascular dementia. Arch Neurol 1996;53: [5] Andreasen NC, Rajarethinam R, Cizadlo T, et al. Automatic atlasbased volume estimation of human brain regions from the MR images. J Comput Assist Tomogr 1996;20: [6] Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D modelbased neuroanatomical segmentation. Hum Brain Mapp 1995;3: [7] Thompson PM, MacDonald D, Mega MS, Holmes CJ, Evans AC, Toga AW. Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. J Comput Assist Tomogr 1997;2: [8] Goldszal AF, Davatzikos C, Pham DL, Yan MXH, Bryan RN, Resnick SM. An image processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomogr 1998; 22: [9] Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain. New York: Thieme Medical; [10] Jack JCR, Twomey CK, Zinsmeister A, et al. Anterior temporal lobes and hippocampal formations: normative volumetric measurements from MR images in young adults. Radiology 1989;172: [11] Kertesz A, Polk M, Black SE, Howell J. Sex, handedness, and the morphometry of cerebral asymmetries on magnetic resonance imaging. Brain Res 1990;530:40 8. [12] Buchsbaum MS. The frontal lobes, basal ganglia, and temporal lobes as sites for schizophrenia. Schizophr Bull 1990;16: [13] Turetsky B, Cowell PE, Gur RC, Grossman RI, Shtasel DL, Gur RE. Frontal and temporal lobe brain volumes in schizophrenia: relationship to symptoms and clinical subtype. Arch Gen Psychiatry 1995; 52: [14] Aylward EH, Augustine A, Li Q, Barta PE, Pearlson GD. Measurement of frontal lobe volume on magnetic resonance imaging scans. Psychiatry Res 1997;75: [15] Fukui T, Kertesz A. Volumetric study of lobar atrophy in Pick complex and Alzheimer s disease. J Neurol Sci 2000;174: [16] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer s disease: report of the NINCDS- ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer s Disease. Neurology 1984; 34: [17] Roman GC, Tatemichi TK, Erkinjuntti T, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43: [18] Neary D, Snowden JS, Gustafson L, et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 1998;51: [19] Michio O, Kubik S, Abernathey CD. Atlas of the Cerebral Sulci 51. Stuttgart: Thieme Verlag; [20] Woods RP, Grafton ST, Watson JD, Sicotte NL, Mazziotta JC. Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomogr 1998;22: [21] Bartko JJ, Carpenter WT. On the methods and theory of reliability. J Nerv Ment Dis 1976;163: [22] Schmidt R. Comparison of magnetic resonance imaging in Alzheimer s disease, vascular dementia and normal aging. Eur Neurol 1992; 32:164 9.