[To be published in a special issue of International Journal on Pattern Recognition and Artificial Intelligence on Processing of MR Images of the Human Brain, edited by Pien, Karl, Kennedy, and Worth]
Neuroanatomical Segmentation in MRI: Technological Objectives Andrew J. Worth, Nikos Makris, Verne S. Caviness, Jr., and David N. Kennedy Center for Morphometric Analysis, Neuroscience Center, Massachusetts General Hospital-East, Building 149, 13th St., Charlestown, MA 02129-2000, USA. http://neuro-www.mgh.harvard.edu/cma/cma.homepage.html Email:
[email protected]
Abbreviated title: Segmentation Objectives
This paper offers a definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses. The main types of difficulties experienced with this problem are described, including those relating to the classification of MR signal intensities and the fact that there is insufficient information in the 2D image. To illustrate the details of obtaining a morphometric description, a case study of semi-automated methods is presented for segmenting the lateral ventricles and caudate nucleus in T1 coronal MR image data. The most significant remaining difficulties are summarized and are offered as objectives for further research. Keywords: neuroanatomical segmentation, quantitative morphometry, neuroanatomy, image segmentation, brain imaging, magnetic resonance imaging.
1.
INTRODUCTION
1 . 1 . OVERVIEW A radiologist analyzing an MRI scan of a patient’s head recognizes various parts of the brain and can quickly examine multiple slice images on the film to locate and identify tumors, stroke, enlarged ventricles, cortical atrophy, and other signs of problems. This kind of qualitative analysis is made using experience and the human visual system. By segmenting neuroanatomical structures in digital MRI data, quantitative three-dimensional morphometrics can be obtained and used as evidence for diagnosis and for assessing response to treatment. Morphometric analysis provides quantitative measures of location, volume, shape and homogeneity of component brain structures. Methods for this kind of computerized analysis have been investigated for over a decade [1-30], but the task is difficult and time consuming due to the large amount of data and the exacting attention to detail necessary for rendering statistically significant results. There is much research on automating the segmentation of MR brain images (for reviews, see [31-34]). While the majority of these methods provide solutions to some of the steps in a quantitative analysis, many cannot be used for practical quantitative morphometric studies because they ignore significant aspects of the problem. The purpose of this paper is to provide a detailed description of the significant problems involved in neuroanatomical segmentation in MRI. We have seen no evidence that there is currently a completely automated method which can always produce good results on practical data. We discuss these terms in more detail and offer a definition of the entire problem in the hope of helping to move towards complete automation. 1 . 2 . PROBLEM DEFINITION Duda and Hart implicitly define segmentation as the figure-ground problem: extracting the figure (object of interest) from an arbitrary picture [35]. Gonzalez and Woods define it as the subdivision of an image into its constituent parts or objects [36]. In these texts from pattern recognition and image processing, the goal is the general processing of images. However, the goal in this work is specifically to obtain quantitative morphometrics of a subject’s brain and not of the image of the brain. The next paragraphs build up a working definition for this specific area of segmentation.
SPECIFIC AND PRECISE NEUROANATOMICAL SEGMENTATION By neuroanatomical segmentation, we mean the extraction of a description of a surface that accurately reflects the morphometry of the subject’s neuroanatomy. By specific neuroanatomical structures, we refer to the requirement in quantitative morphological studies that specific brain structures be measured. By precise, we require that the neuroanatomical structures of interest are specified to a fine level of detail. In quantitative studies, it is not sufficient merely to classify image voxels as belonging to (for instance) three classes: gray, white, and cerebral spinal fluid (CSF). Beyond this, each particular neuroanatomical structure of interest must be located and its extents must be delineated in three dimensions. COMPREHENSIVE NEUROANATOMICAL SEGMENTATION A comprehensive segmentation of gray matter, white matter and CSF regions is desirable since it is often not clear in advance which structures may be of interest. This is because the brain is a complicated, highly interconnected and convoluted structure, much of its functional operation is unknown, and the anatomical literature is often contradictory in its details. In this paper, the goal that the segmentation be as comprehensive as possible is defined both in terms of total coverage and thorough subdivision. For volumetric calculations, total coverage means that all voxels of the brain scan should be accounted for by some named neuroanatomical structure. Additionally, there should be a thorough subdivision of the volumetric data so that the resulting measurements of shape and size refer to specific structurally and functionally important brain regions.
However, there is a trade-off between the number of
neuroanatomical structures segmented and the amount of effort that can realistically be put into not only segmenting, but also defining the structures. Moreover, practical limitations are also enforced by the resolution and tissue contrast available from MRI. An example of a comprehensive segmentation is described in [37]. Here the neuroanatomical regions of interest include: cortical gray matter, subcortical white matter, lateral, third and fourth ventricles, caudate, putamen, globus pallidus, hippocampus-amygdala complex, thalamus proper, ventral diencephalic complex (including hypothalamus and subthalamus as well as medial and lateral geniculate nuclei), brainstem, cerebellar cortex and cerebellar central mass. As automation increases, segmentations can become even more comprehensive.
ROBUST AND PRACTICAL NEUROANATOMICAL SEGMENTATION Neuroanatomical segmentation must be robust enough to produce acceptable results for an appropriate range of the quality of available data. In other words, good results must be attainable in the presence of some amount of both random signal noise and normal anatomical variation. Furthermore, in order to be practical, the analysis must also be performed using a reasonable amount of time and other resources. NEUROANATOMICAL SEGMENTATION: WORKING DEFINITION With the goal of quantitative morphometry, we submit the working definition of the problem of neuroanatomical segmentation as the extraction of a specific, precise and comprehensive 3D morphological description of the neuroanatomical structure of the subject’s brain that is obtained robustly and practically from volumetric image data. 1 . 3 . SIGNIFICANCE QUANTITATIVE MRI IS USEFUL FOR STUDYING THE BRAIN AND DISEASES Magnetic resonance imaging provides a highly efficacious means for observing brain anatomy. A morphometric analysis in conjunction with neuropsychological, neurological, and psychiatric observations and coupled with functional neuroimaging can then be used to aid in answering broad classes of questions about brain structure and function for both normal subjects and patient populations [26, 38]. For example, quantitative brain measurements have contributed to the study of developmental language disorders and autism [39], Alzheimer’s disease [40-43], dyslexia [44, 45], attention deficit hyperactivity disorder [4548], schizophrenia [49], multiple sclerosis [50, 51], Huntington’s disease [52], obsessive compulsive disorder [53], tricotillomania [54]. Precise delineation of cortical parcellation units can be used to locate active brain regions in functional neuroimaging studies [55] and the localization of white matter parcellation units is useful for treatment planning for brain damage such as that caused by stroke [56]. SEGMENTATION IS THE BOTTLENECK In order to provide quantitative neuroanatomical measurements and localizations, structural brain scans must be segmented. And in order to produce statistically significant results for specific neuroanatomical
structures, this segmentation must be precise, comprehensive, robust and practical (as defined above). Even before obtaining any brain scans, this is a difficult problem because it requires the specification of anatomical definitions for the surfaces of a large number of structures as they appear in MR images. The analysis of each brain scan involves the examination of large amount of data and can take a great deal of time. For instance, each brain scan used in a recent study on schizophrenia involved roughly 60 slices (almost 8 megabytes per scan) and, working full time after months of training, each researcher was only able to perform a comprehensive segmentation and gray matter parcellation for around 50 scans per year [57]. Furthermore, segmentation must be performed multiple times on a small subset of scans so the study’s reliability can be measured [58]. Segmentation therefore has a far greater cost compared with the subsequent volumetric analysis [25] and anatomic shape analysis [59] which can be performed with nearly no human intervention. This makes segmentation the “bottleneck” in quantitative morphometric analysis. THE ADVANTAGES OF AUTOMATION The advantages of automating segmentation include increased reliability, consistency, and reproducibility. However, the most practical benefit is that while the computer may work longer at the task, it takes less human time. This will either decrease the cost of a volumetric study, allow more subjects to be analyzed, or since the time commitment of manual methods is prohibitive, it can also allow even more comprehensive analyses. Automated methods produce more reproducible results compared to manual methods because when they do work, they work in the same way. Consistency improves because automatic routines act as a completely unbiased observer. Reliability improves in that automation helps reduce errors that arise because the task is tedious and repetitive. Errors related to fatigue can appear both as a result of many hours of concentration in a given day, and also as other changes in results which can appear over months of segmentation as the segmentor refines his or her skills. A comprehensive, reliable, consistent and reproducible analysis that takes a reasonable amount of time can lead to new levels of statistical significance and even new classes of questions about brain structure and function. Furthermore, as correlations between brain structure and the function, diseases, and pathologies of the brain are discovered, the decrease in cost and increase in availability brought by automating
morphological analysis will also have clinical benefits in diagnostics and the planning and analysis of treatment.
2.
DIFFICULTIES IN NEUROANATOMICAL SEGMENTATION
The difficulties encountered in automating neuroanatomical segmentation (as defined above) fall into 3 general classes: problems related to signal intensities, problems related to the shape of specific structures, and MR imaging problems. In the following discussion we examine neuroanatomical segmentation from an image processing perspective. We ignore most details involving how the image is obtained and delivered for processing. Our starting point is the MR image data as obtained from the scanner and we refer to this as the “raw” image data. We assume we have little control over the scanner’s imaging parameters but we do assume that the image is of sufficient quality that its structure would be visible to someone qualified in reading MR brain scans. For data that does not meet a specified quality criterion, the subject can be re-scanned. We attempt to consider MR images generically but as evident in the case study below, our main interest is in segmenting T1 brain images. 2 . 1 . THE CLASSIFICATION OF MR SIGNAL INTENSITIES PARAMETER ESTIMATION: INTENSITY IS NOT AN ABSOLUTE MEASURE OF TISSUE Perhaps the most basic issue to deal with when performing segmentation of MR brain images is to arrive at an estimate of the intensities that make up the neuroanatomical structures of interest. This is not an easy problem since the relationship between intensity and tissue type is modulated by many factors. However, relative intensities are roughly indicative of tissue type (or else MR could not be used to image the brain). Therefore, this is basically an intensity classification problem. Intensity histograms of T1-weighted brain images tend to have peaks for the background, gray matter and white matter. Figure 1 (a) shows a typical histogram, in this case taken from Figure 1 (b). For a given scan, segmentation parameters such as the gray-white intensity threshold must be determined since different MR scanning parameters produce different absolute signal intensities, different contrasts and also different overall scan quality [60]. Scans obtained from different MRI machines (or the same machine at different times) can also produce differences in the resulting image. Segmentation becomes
easier when the raw image data is better. Better image data can be obtained by optimizing the MRI protocols and pulse sequences [61], and by calibrating and using state of the art equipment. 2000 1800 1600 1400 1200 1000 800 600 400 200 0 1000 1100 1200 1300 1400 1500 1600 1700
(a) (b) Figure 1: Typical histogram. The histogram in (a) was taken over the T1-weighted coronal slice shown in (b). The slice was cropped to exclude most of the non-brain regions of the image. The large peak in the middle of (a) corresponds to gray matter and the smaller peak to the right of the gray peak corresponds to white matter.
OVERLAPPING INTENSITIES Another problem in determining segmentation parameters is encountered when segmenting specific neuroanatomical structures as opposed to segmenting into only gray, white, and CSF classes. Different brain structures have different tissue characteristics which results in a distribution of signal intensities. The range of signal intensities for certain brain structures often overlap. For example, Figure 2 (a) shows histograms of cortex, white matter and the caudate nuclei for the slice shown in Figure 2 (b). The caudate is a central gray-matter structure that has an intermediate average intensity. A substantial overlap can be seen in the histograms. 1000 900 800
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(a) (b) Figure 2: Overlapping intensities. The histogram for the caudate nuclei shown in (a) overlaps in intensity with the histograms for cortex and white matter. The coronal slice in (b) shows both the right and left caudates.
NON-BRAIN INTENSITIES CAN BE SIMILAR TO BRAIN INTENSITIES Many non-brain voxels have similar intensities as brain voxels and therefore make estimating brain intensities more difficult. For instance, muscle in the neck and fatty tissue in the scalp can have the same intensities as gray and white matter. This is certainly a problem for coronal and sagittal images and also for some axial slice images but the effect is minimal on the middle range of axial slices. One technique used to help determine segmentation parameters is to remove all non-brain voxels from the image. However, separating brain an non-brain regions has difficulties of its own as seen in Figure 3. The upper black arrow in this figure points to where meninges adjoin the cortex such that the normally correct threshold (the white outline) does not separate it from the cortex. The lower black arrow points to another location where the threshold for the cerebral exterior allows the segmentation to “leak out” into surrounding non-brain tissues. The problem here is that at the given resolution, non-brain tissues appear to be in contact with the brain.
Figure 3: Problems in removing non-brain voxels. The white outline shows an iso-intensity contour which is at the correct intensity for the exterior of the brain. The two black arrows show where a single intensity cannot be used to locate the exterior of the brain.
STRUCTURAL CHANGES WITHIN AND BETWEEN STRUCTURES There is often a spatial correlation of intensity change within an anatomically defined region that is caused by an underlying change in the composition of the neuroanatomical structure. One example is that white matter is brighter at the corpus callosum than in other areas because the fiber bundles are more concentrated and coherent in direction. Another example is the thalamus which is composed of various nuclei and white matter tracts. In this structure, gray matter and white matter are intermixed to varying degrees. The amount that structural changes shows up in the MR image as signal or noise depends both
on the resolution of the scan and also on the definition of what is to be segmented. For instance, the internal structure of the thalamus may be considered noise if only the exterior of the thalamus is to be segmented. Furthermore, this change in the constitution of a given neuroanatomical structure is usually more pronounced at its boundaries with neighboring tissues. Consider the transition between gray and white matter at the cortex; the MR signal intensity depends on the details of the transition of the tissue from being mostly myelinated axons into mostly cell bodies, and this transition is not necessarily constant [62]. INTENSITY INHOMOGENEITIES Perhaps the most important effect that changes the absolute intensities for a given tissue type in different locations is the presence of large-scale intensity gradients caused by radio frequency (RF) field coil inhomogeneities (for an excellent discussion of this “shading artifact” see [33]). This may not preclude a qualitative visual analysis since the relative intensities of different tissue types make the image appear similar. However, it does necessitate different intensity thresholds to classify tissue types as the voxel location changes both within an individual slice and also across slices. Figure 4 (a) shows an intensity plot along the line drawn in Figure 4 (b) (from left to right). Figure 4 (c) shows how the intensity can change from slice to slice by presenting an intensity plot along the line drawn in Figure 4 (d). The intensity for this scan changes rather dramatically as compared with the majority of MR scans. However, more subtle gradients that are usually imperceptible to the naked eye are a common feature of MR scans. Jernigan, Press, and Hesselink state that “Signal intensity variations due to inhomogeneity of the magnetic field and imperfect radio frequency pulses render simple image analysis techniques using signal thresholds unreliable” [3]. Recent work where the effect of intensity inhomogeneities is removed using homomorphic filtering is described in [63]. DeCarli et al. have a method involving local histograms [22]. Zijdenbos, Dawant, and Margolin describe practical techniques for dealing with intensity inhomogeneities [64]. Bland and Meyer describe a technique which is insensitive to intensity inhomogeneities because it uses gradient information [65]. Wells et al. describe a method for removing the gradient and performing segmentation at the same time [66]. Rajapakse et al. present a technique for segmentation along with the removal of intensity
inhomogeneities and also estimate the tissue class parameters [67]. The biggest danger in removing intensity inhomogeneity, however, is that some of the useful signal may also be removed.
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(c) (d) Figure 4: Intensity inhomogeneities. The graph in (a) shows the intensity from the lower right to the upper left along the line in the coronal slice in (b). The intensities on the left side of (a) are significantly brighter than on the right. The intensities in the graph in (c) show a brightening of the middle slices of the scan along the line in (d). The sagittal image in (d) was created by interpolating between a single column of voxels in each coronal slice image.
2 . 2 . INSUFFICIENT INFORMATION IN THE IMAGE Beyond difficulties which arise from a given structure's characteristic intensities, other difficulties in locating borders can be caused by that structure’s shape, composition, size, and orientation. Extra-image information must be used if the neuroanatomical structure does not have a corresponding change in MR signal intensity in that image. DISCONTINUOUS EDGE INFORMATION When a sharp intensity change is present in an image, it may be able to be used to help locate a boundary between neuroanatomical structures. However, even if this type of edge information is present, it is usually does not provide a whole, continuous boundary. Methods for dealing with discontinuous edge information include edge detection and linking [68], snakes [69, 70], and active surfaces [21, 47, 71]. The last two are excellent examples of the segmentation of specific neuroanatomical structures for large
numbers of subjects. Finite element modeling and statistical modes of variation are described in [72]. Staib and Duncan use Fourier surfaces in [73]. Multiresolution wavelet basis have also been proposed [74, 75]. Székely et al. present a Fourier parameterization of shape along with a set of eigenmodes of parameters characterizing shape variation [76]. A recent review of deformable models in medical image analysis can be found in [77]. STRUCTURE SHAPE AND COMPOSITION The shape of each individual brain is different from all others. While the thalamus and other central structures are more consistent in their shape, the cortex is very different from brain to brain [78]. This can be observed as blurring in “averaged” brains [79, 80]. The presence of normal anatomical variation makes the identification of specific neuroanatomical structures more difficult. Another difficulty arises when the border of a structure is defined in relation to its shape. This happens, for instance, when a researcher is interested in comparing the volumes of the two halves of the brain; the corpus callosum must be divided where there is no intensity border. Extra-image information is necessary for any neuroanatomical structure that is indistinguishable in MR imaging from its surrounding tissues. An example of two structures which are not separated by an intensity border are the caudate and the accumbens (as described in the case study below). STRUCTURE SIZE AND ORIENTATION VS. RESOLUTION A problem occurs where the spatial resolution of the image is not sufficient to resolve discrete tissue regions completely. Borders cannot be placed around nuclei or fiber bundles (or small parts of large structures) which are small relative to the size of the voxels. A prevalent example of this involves the ventricles where there is a sharp white matter/CSF transition. In this case, there appears to be a thin strip of gray matter between the white and CSF. This is particularly a problem near gray matter structures (such as the caudate nucleus) which are located between the ventricle and white matter. The case study below describes this situation in detail. Insufficient resolution also affects the borders between the left and right cerebral hemispheres and also between the borders between the cerebrum and cerebellum. These borders can be difficult to locate in some places because on both sides of these boundaries tissues with similar intensity values are in close proximity; there is insufficient resolution to see where one region of gray
matter ends and the adjoining one begins. Also, the septum pellucidum (the membrane dividing the lateral ventricles) can be thin and nearly disappear. Furthermore, low resolution can make it difficult to segment small lesions in multiple sclerosis patients [63]. Insufficient resolution is one cause of partial volume voxels. The “partial volume effect” is when the volume sampled by a single voxel contains more than one kind of tissue type. This results in an intensity that is between those of the individual tissue classes. An excellent description and review of this problem is given in [33]. Insufficient resolution will cause a number of voxels to be misclassified at structure boundaries with any resolution but as the resolution increases, this number can be made to decrease. Another cause of partial volume voxels is the coincidence in location of the slice acquisition plane with the surface of a neuroanatomical structure. This causes the voxels in a relatively large region of an image to sample both sides of a boundary. This occurs, for instance, when a slice bisects a sulcus and also at the tips of structures which are oriented perpendicularly to the slice image plane. An example of the latter is the horns of the lateral ventricles when image data is acquired in coronal slices since the ventricle’s main axis is rostral-caudal. This explains why misclassification due to partial voluming is at a maximum whenever the major axis of the neuroanatomical structure of interest is parallel to the acquisition plane [25]. In their seminal early work, Lim and Pfefferbaum examined neighboring voxels to decide how to classify a given partial volume voxel [5]. Choi, Haynor and Kim introduced the “mixel” model of a partial volume voxel [81]. Santago and Gage fit mixture density models to the histogram of brain data [82]. Laidlaw points out that mixtures effectively treat each voxel as a single point and he improves the model to include a distance from the voxel center to the boundary between two tissues [83].
3.
CASE STUDY: LATERAL V ENTRICLES AND C AUDATE N UCLEUS
A case study on segmenting the lateral ventricles and caudate nucleus is presented in this section. The manual and semi-automated methods are described to illustrate the specific details of obtaining a morphometric description of the lateral ventricles and caudate nucleus from coronal, T1-weighted MR image data. This description is given in subsections of increasing detail. The remainder of the last
subsection may be skipped by the more casual reader once a feel for the level, complexity, and tediousness of anatomical details and their interactions has been understood. The entire description is included for completeness. 3 . 1 . MRI IMAGE ACQUISITION Typical MRI scans are acquired for the Massachusetts General Hospital's Center for Morphometric Analysis using a 1.5 Tesla General Electric Signa. Contiguous three-dimensional coronal T1-weighted spoiled gradient echo (SPGR) images of the entire brain are attained with an approximate voxel size of 1 mm by 1 mm (in plane) by 3.0 mm (slice thickness) and the following parameters: TR = 40 msec, TE = 5 msec, flip angle = 40 degrees, field of view = 24cm, matrix = 256x256, and averages = 1. 3.2.
POSITIONAL NORMALIZATION
Images are positionally normalized by imposing a standard three-dimensional brain coordinate system on each 3D MR scan. The midpoints of the decussations of the anterior and posterior commisures and the midsagittal plane at the level of posterior commisure are used as points of reference for this affine transformation [84, 85]. These two points and plane are chosen manually. The repositioned scans are then resliced into normalized 3.0 mm coronal scans which are used for subsequent analyses. 3 . 3 . GENERAL SEGMENTATION METHOD At the Center for Morphometric Analysis, manually guided neuroanatomical segmentation is performed using a program written for Sun workstations called “Cardviews”. Users are presented with the three cardinal views of the MR data set: coronal, sagittal, and axial. The brightness and contrast of the image can be adjusted so that the intensities of interest for a given neuroanatomical structure may become optimally perceptible. Using the mouse, isointensity contours are chosen and boundaries are drawn to surround each neuroanatomical structure. These are then grouped into a continuous outline around the structure and the final step is to assign an anatomical label the outline and save the result. A more detailed description of the segmentation may be found in [37].
3 . 4 . SEGMENTING VENTRICLE AND CAUDATE For each slice, first the lateral ventricle, and then the caudate nucleus are segmented separately for the right and left hemispheres. Where the border around the ventricle adjoins the caudate nucleus, it also serves as part of the border for the caudate. The rest of the caudate border is usually determined by an iso-intensity contour using the intensity that is halfway between the caudate (which is gray matter) and the surrounding white matter. Since the intensity value for the ventricle contour is usually different from the intensity of the caudate contour, the junction of these contours at the superior and inferior tips of the caudate are problematic. The exact locations of these junctions do not correspond with the extent of the caudate, but rather are determined by partial volume voxels. Manual drawing is used to define the superior and inferior extents of the caudate. Furthermore, the caudate-accumbens and caudate-putamen borders must always be hand-drawn since there is no intensity difference between these gray matter structures. The caudateaccumbens border is created using a slightly oblique line from the inferior edge of the lateral ventricle laterally across the gray matter and to the point where the internal capsule ends. The border between the caudate and the putamen is defined as a vertical line straight down from the most inferior part of the internal capsule to the striatal/white matter border. The tail of the caudate which appears just above the hippocampus becomes too small (with respect to the voxel size) to be seen clearly in 3mm slices and therefore is not segmented. 3 . 5 . SPECIFIC SEGMENTATION DETAILS There are roughly five “phases” of the segmentation of the lateral ventricle and caudate nucleus: (I) lateral ventricle anterior horns, (II) caudate head, (III) caudate body, (IV) lateral ventricle junction, and (V) lateral ventricle posterior horns. Figure 5 is a sagittal sketch showing the approximate range of coronal slices for the five phases. Each of these will be described along with the problems found in them. PHASE I: LATERAL VENTRICLE ANTERIOR HORNS Figure 6 shows phase I. The sagittal image at the top shows the locations of four coronal slices in this phase. Starting anteriorly (from the left side in the sagittal image), there is initially only white matter where the anterior tip of the lateral ventricles and the genu of the corpus callosum will eventually appear.
Figure 6 (a) has a sketch showing how the first hint of the ventricles can appear (the dashed ovals) relative to the top of the corpus callosum (the dashed “X” in the figure) which will be used as a landmark in the remaining figures. The voxels inside the dashed lines contain both white matter and CSF and therefore have intermediate intensity values. Figure 6 (b) shows a sketch representing the ventricles when they finally appear without partial voluming. The solid dot represents the top of the corpus callosum (as seen in a coronal slice). Next to the sketch, the same coronal image region is presented both with and without the outlines of the ventricles. The genu (anterior end) of the corpus callosum appears in this first phase. Next, the head of the caudate nucleus makes its first (partial volumed) appearance as depicted in Figure 6 (c). This can cause fuzzy borders on the medial and inferior lateral ventricles. Figure 6 (d) represents the head of the caudate which can have partial volumed borders on all sides as it first appears. Also, in successive caudal slices as the lateral ventricles appear medially and eventually connect, the septum pellucidum can be very thin and may nearly disappear as a result of partial volume voxels containing both white matter and CSF. PHASE II: CAUDATE HEAD Figure 7 shows a sagittal MRI image and three representative sketches and coronal MRI slices representing phase II. Figure 7 (a) contains a sketch of the head of the caudate after it appears anteriorly. The inferior boundary must be drawn without the aid of an intensity difference where the caudate joins the nucleus accumbens (medially) or the putamen (laterally). Figures 7 (b) and (c) illustrate the decrease in size of the caudate head in the following slices. In these slices the caudate borders white matter and ventricle. However, at the superior and inferior tips the caudate may form a three-way junction with CSF and white matter. Here, as described earlier, the boundary must be drawn by hand. Figure 7 (c) shows how the inferior border of the ventricles becomes fuzzy due to partial voluming, the presence of choroid plexus, and also the joining of the lateral and third ventricles. In these cases, the border may need to be drawn by hand. PHASE III: CAUDATE BODY Figure 8 illustrates the position and shape of the caudate as it decreases in size along its body in Phase III. In Figure 8 (a), the lateral ventricle may connect with the third ventricle and the caudate may border the
thalamus. These boundaries must be drawn by hand. In regions represented by Figures 8 (b), while partial volume is not usually a problem (since the caudate passes nearly orthogonally through these slices), the inferior border of the ventricle may be fuzzy due to the presence of choroid plexus in the ventricle. Eventually, the caudate can no longer be clearly found as it continues to shrink in size. As shown in Figure 5, the inferior horn of the lateral ventricles are also present in phase III. Segmenting this part of the ventricles presents similar problems but their discussion is beyond the scope of the present article. PHASE IV: LATERAL VENTRICLE JUNCTION Figure 9 shows a representative range of slices where the inferior horns of the lateral ventricles join the body (phase IV). The portions of the caudate nucleus present in these slices (its genu) is severely partially volumed with the lateral ventricle and is not segmented. In Figure 9 (a), the inferior border of the ventricles is shown as jagged because partial voluming and the presence of choroid plexus can make this section difficult to locate. Figure 9 (b) represents the location where the inferior lateral ventricle joins with the ventricle body. Since the lower border of the ventricle body drops to become the superior border of the inferior lateral ventricle, partial volume voxels make up an extensive amount of voxels in this slice so that in order to properly place the border, the user must take the 3D shape of the ventricles into consideration. Choroid plexus can also be present in this slice and also in the slice sketched in Figure 9 (c) which shows the ventricle posterior to the junction. PHASE V: POSTERIOR HORNS Figure 10 shows phase V; the posterior horns of the lateral ventricles. Figure 10 (a) shows continued presence of choroid plexus, but only at the beginning of the posterior horns of the lateral ventricles. The remaining slices (10 (b) and (c)) do not contain choroid plexus but the final slices have partial volumed voxels where the ventricles end. The dashed-X in Figure 10 (b) shows that the corpus callosum ends in this region.
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Figure 5: Five Phases. This sagittal sketch shows the approximate range of coronal slices for each phase of the lateral ventricle and caudate nucleus. The solid structure in the middle is the corpus callosum (CC). The caudate nucleus is shown tucked under the corpus callosum. The black outline is the projection of the lateral ventricles.
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Figure 6: Phase I: Anterior Horns. Successive coronal sketches and corresponding coronal regions of the ventricle and caudate are shown here with and without outlines where the structures are present. The sagittal slice at the top shows the locations of the coronal slices in (a)-(d). The dashed-X in (a) shows where the center of the corpus callosum will appear. When present in a slice, the corpus callosum is represented by a dot as shown in the sketches in (b)-(d). The ventricle is the dark filled outline in the sketches while the caudate (where present) is the lighter filled outline. See the text for a description of the changes in shape. The coronal slice region in (a) contains no outlines since the lateral ventricles do not appear until (b). In the coronal slice region in (d), the ventricles are the relatively darker structures and the partial-volumed caudate nucleus is below and attached to the ventricles
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Figure 7: Phase II: Caudate Head. The sketch in (a) represents the case where there is no intensity border between the caudate and the putamen. See the text for more details.
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Figure 8: Phase III: Caudate Body. The black marks in (b) indicate the potential presence of choroid plexus. This is seen in the images in (a) and (b) as relatively lighter material inside the ventricles.
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Figure 9: Phase IV: Ventricle Junction. See text for details. The choroid plexus is explicitly drawn in black in (c) though it may be visible in all slices of this phase. The image regions in (a) and (b) are on either side of where the inferior lateral ventricle joins the lateral ventricle body
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Figure 10: Phase V: Posterior Horns. The black marks in (a) inside the ventricle again indicate the possibility of finding choroid plexus. The dashed-X in (b) and (c) indicates that the corpus callosum is no longer present.
4.
DISCUSSION
4 . 1 . MANUAL SEGMENTATION IS STILL PERFORMED Tsai, Manjunath and Jagadeesan, as part of the introduction to their work on an automated method for segmentation say that, “many of the current algorithms provide interactive tools to achieve segmentation,
but such a solution is not going to be feasible given the large amount of data to be processed.” [86] This is at odds with the large number of studies that have been performed using manual methods. In surveying MRI studies evaluating the structure of schizophrenic brains, Chua and McKenna state, “... regions of interest have had to be identified by drawing round them manually; as yet there is no computerized method for separating grey and white matter” [87]. In fact, the vast majority of groups cited in the introduction who are actively pursuing research on quantitative morphometrics on MR brain images still employ a nontrivial amount of manual editing of segmentations, especially to obtain precise results for specific neuroanatomical structures. Therefore, it appears that these two perspectives (research using and research on segmentation) could profit by more intercommunication. Additional evidence that something is preventing the propagation and use of automated methods is found in the excellent review of nearly 200 MR brain segmentation papers by Alex Zijdenbos and Benoit Dawant. These authors point out that, “Although many of the MRI segmentation techniques surveyed in this paper appear to be practically useful, they are predominantly found in research laboratories and rarely used in clinical studies” [33]. If there are many automated methods that have been demonstrated in the literature, then why is there still so much manual segmentation being performed? We believe that there are two main reasons; (1) there are still unsolved problems in attaining fully automatic precise neuroanatomical segmentation, and (2) automated systems have not been thoroughly validated and sufficiently disseminated. UNSOLVED PROBLEMS IN AUTOMATION It cannot be denied that automation of the segmentation of MR brain images is a difficult problem. Bezdek, Hall, and Clarke further state that, “Efforts to reduce the dependency of MR segmentation on humans will probably never produce a fully automated process” [32]. However, given that there are published accounts of achieving automatic segmentation (e.g. [66, 86]) the question, “can neuroanatomical segmentation be fully automated?” really reduces to asking how precise are the resulting segmentations and how completely automated is the process? It also depends on the definition of the problem of segmentation. In many works referenced above (e.g., see [34]), segmentation refers to intensity-based classification. However, the definition for neuroanatomical segmentation given in the present paper goes beyond classification to include the identification of specific neuroanatomical structures. Furthermore, the
semi-automated case study described above for segmenting the ventricles and caudate involves fine grained anatomical knowledge and a detailed set of steps for the extraction of boundaries. To our knowledge, there is no automated method which takes all of this information into consideration, and as a result, there seems to be no automated method capable of attaining the same level of precision as these manual methods. Therefore, automatic neuroanatomical segmentation is still an unsolved problem. LACK OF VALIDATION A method must not only be able to work, it must be demonstrated to work well on a particular problem of interest. Zijdenbos and Dawant state that the segmentation techniques they surveyed are predominantly only found in research labs and that they are not accepted for clinical uses because of the lack of adequate validation studies [33]. Their paper and the survey by Clarke et al. [34] provide good discussions of the difficult problems involved in validation, the absence of a “gold standard”, and methods and metrics for comparing results. That there is a lack of adequate validation seems still to be generally true, but the situation is improving. As an example, more than 1000 multiple sclerosis brain scans were segmented by Wells et al. [66]. These investigators also present quantitative comparison with manual segmentations. LACK OF APPLICABILITY Another understandable reason for the lack of dissemination of automated methods is that any given automated analysis is usually tailored for specific data such as scan orientation, acquisition parameters, etc., and also tailored for a specific goal. The requirement of adapting the method to a new case may be prohibitive for a lab without the necessary qualifications and resources. As the complexity of technological solutions increases, clinics and labs may not be equipped in terms of computer power, connectivity, technical skills, software, and time. 4.2.
ABSOLUTE ACCURACY
Segmentation results can be validated by using hardware and software phantoms, by postmortem studies, by using fiducial markers, and by scanning the same subject multiple times in multiple modalities, but each of these methods provides only a limited validation because of dissimilar contrasts, insufficient shape complexity, or other differences from data taken from living brains. Note that the lack of a gold standard
means that there is no way to judge the absolute accuracy of a segmentation, and it is therefore inappropriate to make claims about the absolute accuracy of segmentation results. One can, however, say something about the overall correctness of a method at some level of precision (the level of detail and specificity of the result), reproducibility, consistency, robustness to specified level of noise or artifacts and also about changes in reliability (i.e. changes in the amount of variability). In obtaining morphometrics it is important to have within-study validation and an estimation of the variability of the results. This can be done by scanning subjects multiple times and segmenting them blind to the purposes of the study. Since the absolute measurements cannot be considered “accurate”, the study should focus on the relative differences between the test population and controls. 4 . 3 . TECHNOLOGICAL OBJECTIVES Having stated a definition for neuroanatomical segmentation, we now suggest a few of the general requirements for solutions. The two core problems are (1) classification of voxel intensities and (2) labeling regions as neuroanatomical structures. These require extra-image information which can be divided roughly into structural knowledge and procedural knowledge. STRUCTURAL KNOWLEDGE Something like the “shape” of the structure need not necessarily be explicitly represented, but there must be ways of using structural knowledge to delineate the structure. Structural knowledge should include: a definition of each structure and how it appears in MRI, a description of the intensity distribution as a function of location within a structure, the location of each structure relative to landmarks or neighboring structures, and expectations about sub-structures within structures. Something must be known about the average structure and its normal variation so that abnormal cases can be recognized. Some kind of “surface” must usually be represented since this is the most common output of the segmentation system. PROCEDURAL KNOWLEDGE The main procedure to be repeatedly performed is the correct location (placement) of anatomical boundaries even where direct intensity information is not available. An important ability is the placement of these borders relative to previously located landmarks, lines and surfaces. Other important procedures
include removing or otherwise circumventing intensity inhomogeneities, handling partial volumed voxels, the ability to recognize configurations or cases so that both normal and abnormal variation may be handled robustly, and the ability to measure the quality of the results.
5.
CONCLUSIONS
We have set out to provide a detailed description of the significant problems involved in neuroanatomical segmentation in MRI. With the goal of quantitative morphometry, the working definition of this problem is the extraction of a specific, precise and comprehensive 3D morphological description of the neuroanatomical structure of a subject’s brain that is obtained robustly and practically from volumetric image data. For a description that comprehensively reflects the morphometry of the subject’s neuroanatomy, all voxels of the brain scan should be accounted for by a neuroanatomical structure which is a specific structurally or functionally important brain region. From an image processing perspective, this is not an easy problem because of difficulties related to signal intensities and because the image does not provide enough information. Classification of MR signal intensities is difficult because: intensity is not an absolute indication of tissue type; intensities overlap; non-brain intensities are similar to brain tissue intensities; and intensity inhomogeneities are present in the image not only due to RF field coil inhomogeneities but also due to structural changes in the tissues. Extra-image information must be added to specify neuroanatomical structure boundaries for: shape related, functionally defined or arbitrary definitions; structures which are too small to appear at the given resolution; structures where partial voluming occurs; and structures which are indistinguishable from surrounding structures. Furthermore, all of this must be done in the presence of some amount of random signal noise and normal anatomical variation. The case study on the lateral ventricles and caudate nucleus presented above illustrates the amount and intricate level of details that must be considered when performing precise neuroanatomical segmentation. A system that automatically does neuroanatomical segmentation must classify intensities while using structural and procedural, extra-image information to define specific structures which may have non-intensity borders. However, even if all of the problems are addressed, the final judgment on the goodness of the solution depends on demonstrating that the results are valid and that the methods are applicable. Therefore, it is
imperative to also consider validation and applicability when designing and demonstrating segmentation methods. This follows from all of the reasons given in the discussion above: that there are still unsolved problems in the automation of precise neuroanatomical segmentation, that no method has been adequately validated for this task, and that it is often difficult to adapt a given method of automation to a specific task. Automated methods for neuroanatomical segmentation must not only produce results with the desired precision, but they must also do so given readily obtainable data for a well-defined clinical application and they must demonstrate this in long term validation studies involving large numbers of subjects. It is our hope that the definition of neuroanatomical segmentation provided by this paper will assist research in pattern recognition and artificial intelligence to increase the automation of this task. Since segmentation is the bottleneck of quantitative MR brain analysis, the benefit will not only be to increase the number of brains segmented, but also an increase in reproducibility, consistency and reliability. Ultimately, this may help to answer new classes questions in brain science and medicine.
ACKNOWLEDGMENT This work was supported in part by grant NS 27950 and NS 34189 from the National Institute of Neurologic Disorders and Stroke and also in part by the Fairway Trust. The authors would also like to thank James W. Meyer for writing “Cardviews”; Julie M. Goodman, Elizabeth A. Hoge, Mark R. Patti, Jason A. Tourville, Jill M. Goldstien, Larry J. Sideman, and Pauline A. Filipek for their valuable contributions to the segmentation methods described herein.
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