Computer-Aided Diagnosis of Lumbar Stenosis Conditions

Computer-Aided Diagnosis of Lumbar Stenosis Conditions Soontharee Koompairojn 1 Kathleen Hua 2 Kien A. Hua 1 Jintavaree Srisomboon 3 1 University of Central Florida, Orlando FL 32816, U.S.A. [soonthar, kienhua]@eecs.ucf.edu 2 Emory University, Atlanta, GA 30332, U.S.A., khua@learnlink.emory.edu 3 BMA General Hospital, Pomprab, Bangkok 10110, Thailand, J_tavare@bma.go.th ABSTRACT Computer-aided diagnosis (CAD) systems are indispensable tools for patients healthcare in modern medicine. Nevertheless, the only fully automatic CAD system available for lumbar stenosis today is for X-ray images. Its performance is limited due to the limitations intrinsic to X-ray images. In this paper, we present a system for magnetic resonance images. It employs a machine learning classification technique to automatically recognize lumbar spine components. Features can then be extracted from these spinal components. Finally, diagnosis is done by applying a Multilayer Perceptron. This classification framework can learn the features of different spinal conditions from the training images. The trained Perceptron can then be applied to diagnose new cases for various spinal conditions. Our experimental studies based on 62 subjects indicate that the proposed system is reliable and significantly better than our older system for X-ray images. Keywords: Feature extraction, Machine Learning, Computer-aided diagnosis, Lumbar spine, Spinal stenosis, MR images, Multilayer perceptron 1. INTRODUCTION Global prevalence of lower back pain could be as high as 42% according to the World Health Organization. It is the second most common neurological ailment in the United States, only headache is more common. Such pain, interfering with work and routine daily activities, is the most common cause of job-related disability and a leading contributor to missed work. Approximately 2% of workers injure their back each year; and this is considered the most expensive ailments inflicted on industrial societies [2][4]. The National Institute of Neurological Disorders and Stroke estimates Americans spend at least $50 billion each year due to low back pain [11]. It is a chief cause of visits to primary care physicians. A common cause of low back pain is lumbar spinal stenosis. The spinal cord and the spinal nerve roots are surrounded by bone structures. Wear-and-tear effects of aging can lead to narrowing of the space around the spinal cord or the nerve roots. This condition is called spinal stenosis. When this condition occurs in the lower back, it is called lumbar spinal stenosis. Lumbar spinal stenosis which compresses on the nerves may cause pain, numbness, or weakness in the legs. Currently, the prevalent systems for lumbar spinal stenosis diagnosis are PACS (Picture Archiving and communication system) and other viewing tools such as Syngo fastview [13]. Radiologists use them to manually draw lines to measure various canal distances. The effectiveness of these tools depends on the knowledge and experience of the user. A system capable of suggesting a diagnosis plan based on data obtained from the history, physical examination, and diagnostic studies in the patient suspected of having a herniated lumbar disc is presented in [7]. To the best of our knowledge, we introduced the only image-based CAD system for LSS in [10]. This system can detect various spinal conditions from X-ray images. First we applied the Active Appearance Modeling [15] technique to automatically label the boundary points of the vertebrae. Then, we adapted the vertebral morphology technique [8] to extract features from the labeling points. With this framework, we built a CAD system with two environments one for managing training images and building the classifiers, and the other environment for diagnosis use in practice. The experiment studies for this system, based on the X-ray image database NHANES II available from the National Library of Medicine, indicate that the system is effective for screening purposes. However, its performance is constrained to the lateral view of lumbar spine X-ray images. In this paper, we introduce a new CAD system that leverages magnetic resonance (MR) images to achieve substantially better

diagnosis quality. In recent years, magnetic resonance imaging (MRI) has become the preferred modality since it scans large areas of the spine without ionizing radiation. Moreover, MRI provides exquisite soft tissue details, is multiplanar and reveals more details of lumbar spine stenosis. The remainder of this paper is organized as follows. In Section 2, we provide background information on the anatomy of the lumbar spine and lumbar spine stenosis. We present our visual features for the characterization of various lumbar spinal conditions, and discuss the proposed automatic feature extraction technique in Section 3. Our method for building the classifier for stenosis diagnosis is introduced in Section 4. The experimental results of the system are given in Section 5. Finally, we give our concluding remarks and discuss our future work in Section 6. 2. BACKGROUND - LUMBAR SPINAL STENOSIS To make the paper self-contained, we give a brief introduction to the anatomy of the lumbar spine and discuss the two types of lumbar spinal stenosis in this section. The normal spine is composed of 24 bony structures called vertebrae. The lumbar spine is the lower portion of the spine structure. It normally consists of five vertebrae; designated L1 through L5, starting at the top. Each vertebra (Figure 1) comprises a vertebral body, lamina, and pedicles, which surround a ring-like space, the spinal canal. Each vertebra has two sets of facet joints - one pair faces upward and one downward. They are hinge-like and connect the adjacent vertebrae together (Figure 2). Between two vertebrae is a gel-like cushion called an intervertebral disc [1][3]. These discs help to absorb pressure and keep the vertebrae from grinding against each other. Facet joints and discs allow the spine to bend and twist. The vertebrae and discs are held together by groups of ligaments (Figure 2). Also illustrated in Figure 2 is the spinal canal. It is the vertical space within the spinal column which contains the spinal cord. Nerves branching from the spinal cord travel to different parts of the body through small openings on the sides of the canal, called intervertebral foramina (lateral canals). Each foramen is formed by the pedicles of adjacent vertebrae above, the vertebral bodies in front, and the articular processes behind (Figure 2). This foramen allows for the passage of the spinal nerve root. Figure 1. Anatomy of a spinal vertebrae Figure 2. Spinal canal and an intervertebral disc between two adjacent vertebrae (image courtesy of St. Joseph s Hospital Health Center).

Lumbar spinal stenosis (LSS) is defined as a focal narrowing of the spinal canal. The symptoms are nerve root pain and/or back pain due to compression of the nerves. As an example, one scenario is as follows. Degenerative disc due to tears can weaken the disc wall. Disc center becomes damaged and loses part of its water content. Unable to act as a cushion, the disc flattens causing facet joints to misalign. This condition may encourage bone spurs. If the spurs grow into the spinal canal, they cause spinal stenosis, and may compress the spinal cord and nerves. In general, spinal stenosis can be applied to any combination of the following root compression mechanisms: Posterior osteophytes - Abnormal bony outgrowth protrude toward the front of the spinal canal. Hypertrophy of ligament and facet - Ligaments and facet joint grow abnormally large and reduce the diameter of the foramen. Disc space narrowing - A damaged disc may lose some of its water content and become thinner. Disc herniation/bulging - Tear in the disc wall may allow the soft central portion of the disc to bulge out. Spondylolisthesis - A forward dislocation of one vertebra over the one beneath can produce pressure on spinal nerves Lumbar spinal stenosis conditions can be grouped into two categories. Stenosis occurring in the central spinal canal where the spinal cord is located (Figure 3) is referred to as central stenosis. More often, stenosis may also occur due to the entrapment and compression of the nerve root in its pathway through the foramen (Figure 3). This is called lateral stenosis [5]. The challenging problem for LSS is that there is little or no evidence to investigate the nerve root compression that occurs. Figure 3. Types of lumbar spinal stenosis (left - image courtesy of Lordex Spine Institute at Lincoln Health Center.) and vertical view of nerve root canal (right) 3. AUTOMATIC FEATURE EXTRACTION The input to our CAD system is a series of T2-weighted MR images of lumbar spine in transverse (axial) views as shown in Figures 4(A). We leave the investigation of the sagittal (side) view (Figure 4(B)) to a future study. Our technique has three major steps: (1) Spinal components segmentation: This task performs image segmentation to recognize the components of spine. (2) Spinal features extraction: This task measures and calculates to extract spinal characteristics such as the diameters and length of the spinal components. (3) Spinal stenosis classification: This task applies a multilayer Perceptron framework to classify new cases according to the spinal features. We discuss the details of the first two steps and a briefly introduce the third step in this section. The details of the third step are given in Section 3.

(A) Transverse view Figure 4. Sagittal view and transverse view of lumbar spine (B) Sagittal view 2.1 Spinal components recognition We applied the Multilayer Perceptron (MLP) framework to develop a fully automatic segmentation technique for the transverse views of lumbar spine. The objective is to segment the image (at its reduced 256192-pixel resolution) into spinal components, each can be examined later to detect a certain spinal condition. First, the system searches around the center of the image for the area of the spinal canal, which is the brightest region of the image. As an example, the intensity histograms of the spinal canal area and the right superior articular facet area are shown in the right and left of Figure 5, respectively. The two histograms look very different. They show that most pixels in the right superior articular facet are dark (i.e., data skew to the left), whereas the spinal canal consists of mostly very bright pixels (i.e., data skew to the right). By exploiting this property, the area of the spinal canal can be recognized by first finding a very bright pixel and then performing image segmentation using region growing. This bright canal area is represented by its minimum bounding rectangle, our first region of interest (ROI) as shown in Figure 6. Based on this ROI, the system derives two additional rectangular regions of interest, the right hand side of the spinal canal (labeled 3) and the left hand side of the spinal canal (labeled 4), as illustrated in Figure 6. Similarly, the ROI corresponding to the bottom of the intervertebral disc (labeled 2) is obtained based on the 3 rd and 4 th ROI s as depicted in Figure 6. These four ROI s are also shown in Figure 7 for a real image. Figure 5. Grayscale histogram show spinal canal (right histogram) has mostly bright pixels and the right superior articular facet (left histogram) has mostly dark pixels.

Figure 6. Four regions of interest Figure 7. Four regions of interest of a real image. Next, the system performs segmentation by examining each pixel of the four regions of interest and assign it to one of the six segmented areas, each corresponds to one of the six spinal components as shown in Figure 8, namely (1) spinal canal, (2.1) bottom of intervertebral disc, (2.2) right superior articular facet (including both the superior articular process and the facet joint), (2.3) left superior articular facet, (3) right ligamentum flavum, and (4) left ligamentum flavum. This is achieved by using three MLP s (Figure 9), one for each of the ROI s 2, 3, and 4. The input to each MLP is the feature vector of the pixel being labeled. This feature vector consists of the following values: the x-coordinate and y-coordinate of the pixel being labeled; 13 intensity values, one for the pixel being labeled and one for each of its 12 neighboring pixels as illustrated in Figure 9; and the intervertebral disc level. Figure 8. Boundaries of spinal components

The intervertebral disc level is included in the feature vector because we use a single MLP for all intervertebral disc levels. Given a larger training set, one can consider training an MLP for each disc level to achieve better performance. Figure 9. Spinal component segmentation using multilayer perceptron The output of the MLP indicates the type of the pixel. The MLP for ROI 2 recognizes pixels of the intervertebral disc, vertebral body, and superior articular facet; the MLP for ROI 3 recognizes pixels of the right ligamentum flavum; and the MLP for ROI 4 recognizes pixels of the left ligamentum flavum. We note that the segmentation of the spinal canal has already been done by region growing as described in Section 2.1. An example of the segmentation result is given in Figure 8. It shows the boundaries of the six spinal components. 2.2 Spinal Feature Extraction After spinal components have been identified, we can measure the spinal features to facilitate spinal stenosis diagnosis. We first determine the five boundary points that outline the posterior border of the vertebra (vertebral body or intervertebrtal disc) as shown in Figures 10 and 11. To facilitate our discussion, we refer to these boundary points from right to left as BP 1 to BP 5, respectively. The 1 st ROI is equally divided into equal partitions by five vertical lines (V 1, V 2, V 3, V 4, and V 5 ), and three horizontal lines H 1, H 2, and H 3, as depicted in Figure 10. BP 2, BP 3, and BP 4, are the intersections between the posterior border of the vertebra and the vertical lines V 1, V 3, and V 5, respectively. The boundary point BP 1 is 14 pixels away from BP 2 along the posterior border. Similarly, the boundary point BP 5 is 14 pixels away from BP 4 along the posterior border. Figure 10. The five boundary points and six spinal canal features The spinal features are measured from the six spinal components as follows: Anteroposterior diameter: This is the distance between BP 3 (i.e., the middle boundary point) and the bottom of the spinal canal along the vertical line V 3 (Figure 10).

Right (Left) canal height: This is the distance between BP 2 (BP 4 ) and the bottom of the spinal canal along the vertical line V 1 (V 2 ) (Figure 10). Transverse diameter: This is the width of the spinal canal along the horizontal line H 2 (Figure 10). Upper (Lower) canal widths: This is the width of the spinal canal along the horizontal line H 1 (H 3 ) (Figure 10) Right (Left) lateral canal diameter: This is the vertical distance between the boundary point BP1 (BP5) at the posterior border of the vertebra and the superior articular facet (Figure 11). This feature measures the diameter of the lateral recess, a region located between the central canal and the intervertebral foramen (Figure 3). Ligamentum flavum thickness: The thickness of this soft tissue can be measured at the facet joint level. There are left and right ligamentum flavum thickness measurements(figure 11). They are the thickest area of the corresponding ligamentum flavum measured along a 40 angel with the anteroposterior diameter. Figure 11. Spinal features used in stenosis diagnosis The aforementioned features are designed to measure the various conditions of both central stenosis and lateral stenosis. The relationship between our spinal features, the types of compression mechanism, and the stenosis categories these features are designed to detect is summarized in Table 1. As an example, the second row of the table indicates that a herniated disc can affect the height of the anteroposterior diameter; and as a consequence, central stenosis can be detected using this feature. Table 1. Spinal features related to Spinal conditions Compression Mechanisms Stenosis Categories Spinal Features Disc Herniation Hypertrophy of Ligaments or Facet Central Lateral Left & Right Canal Heights Anteroposterior Diameter Transverse Diameter Upper Canal Width Lower Canal Width Lateral Canal Diameter Ligamentum Flavum Thickness

2.3 Spinal Stenosis Classification After extracting spinal features from the training MR images, this information is verified by the radiologists and used to train one Multilayer Perceptron (MLP) classifier, as shown in Figure 12, for each of the four stenosis conditions (Table 1). The input to this MLP is the set of spinal features, and its output indicates the positive or negative result of the diagnosis. We shall discuss this MLP in greater detail in the next section. 4. BUILDING THE CLASSIFIER MLP is a feedforward network of artificial neurons, which maps the input data onto a set of desired output [6][14[16]. Our MLP consists of a layer of input neurons, a layer of output neurons, and a hidden layer. The neurons of any two consecutive layers are connected together as a bipartite graph as illustrated in Figure 12. Similar to many other supervised learning techniques, an MLP can deduct a function from training data. The goal of the learning process is to predict the value of the function for any valid input object (i.e., a feature vector in our context) after having seen a number of training examples. This is achieved by finding the set of weight values for the links that will cause the output from the neural network to match the desired target values as closely as possible. This process is based on a gradient descent (an error minimization) technique we shall describe shortly. Figure 12. A Multilayer Perceptron for spinal stenosis classification. 3.1 MLP learning process The basic MLP learning procedure is as follows. 1. All weights of the links between nodes are set to random numbers. 2. The network learns from the training data instance to calculate the output values. 3. These output values are compared to the target values (from the training set). 4. The difference between the output values and the target values are propagated backwards, and all the weights are adjusted accordingly. 5. Repeat Steps 2-4 for each data instance in the training set to complete the training. The adjustment performed in Step 4 is based on a gradient descent technique. Consider an error surface with respect to all the weights. This surface shows the amount of error that would result for each combination of weights. The bottom of this error surface gives the optimal set of weights. The learning challenge is to find the set of weights that produce the minimum error for the whole training set. The gradient descent technique uses the error gradient to descend this error surface. This gradient is the slope of the error surface which indicates the sensitivity of the error to changes in the weights. This sensitivity can be exploited to incrementally guide the changes in the weights towards the optimum.

3.2 MLP parameters In our performance evaluation, we used the MLP in WEKA data mining software [12]. There are several parameters that we needed to set up. We discuss these parameters below; and their values used in our experiments will be given in Section 4. 1. Learning rate: This is the amount the weights are updated. The weight is updated as follows: W mk t + 1 = W mk t + αe k where W mk is the weight of the link from node m to node k of two consecutive layers, t is the time at that moment, α is the learning rate, and E k is the error function of node k. 2. Momentum: This factor is applied to the weights during updating. It is used to avoid oscillating weight changes. Therefore, the weight would not stay the same. 3. Training time: This is the number of iterations of the training session. 4. The validation threshold: This threshold is used to terminate validation testing for over fitting. It dictates how many times in the row the validation set error can get worse before training is terminated. 5. The number of nodes in the hidden layer (N H ): For our experiment, Where N H is the number of hidden nodes, N I is the number of input nodes, and N o is the number of the output nodes. N H = N I + N o 2 5. EXPERIMENTAL RESULTS We evaluated our system on the lumbar spine MR images in transverse view of 50 subjects. The contrast of the MR image can be controlled through the pulse sequence parameters. There are two common pulse sequences, T1- weighted sequence and T2-weighted spin-echo sequences. Our MR images were generated using T2-weighted sequence with a long TR (1000ms TR 2500ms, mostly 1290) and a short TE (25ms TE 30ms, mostly 26). The 50 clinical MRI volumes include both normal and abnormal vertebrae. The clinical diagnosis reports are obtained from Bangkok Metropolitan Medical Center, Prasat Neurological Institute, Rajavithi Hospital, all in Bangkok, Thailand. These reports were generated by agreement between at least one radiologist and one orthopedist. Without loss of generality, we consider only female patients in this study. Male patients typically have larger spinal structures and the same technique can be applied to them separately. The ages of our patients range between 18 and 74, with a mean of 48. In our experiments, we focused on the four conditions that could be examined in the transverse view of the spine, namely (1) hypertrophy of ligamentum flavum and facet, (2) disc herniation, (3) central spinal stenosis, and (4) lateral spinal stenosis. We investigated the four intervertebral discs between vertebra L2 and the first sacral vertebra S1 (Figure 13). These intervertebral discs are called L2-L3, L3-L4, L4-5, and L5-S1, respectively. The number of verified positive cases for each spinal condition in the experimental data set is given in Table 2. These clinical diagnosis data are generated under the collaboration between radiologists and orthopedists. We present the performance of the segmentation technique and the quality of the diagnosis in the following two subsections.

Figure 13. The four intervertebral discs considered in the experiments (image courtesy of Taiwan Spine Center). Table 2. Number of positive cases for each spinal condition at differenct disc levels in the data set Intervertebral disc level Spinal conditions L2-L3 L3-L4 L4-L5 L5-S1 Hypertrophy of ligamentum flavum & facet 5 12 28 18 Disc space narrowing 8 16 24 24 Disc Herniation 12 27 39 31 Central spinal stenosis 18 26 36 29 Lateral spinal stenosis 4 19 35 23 4.1 Performance Metrics We evaluate the correctness of the segmentation results (extract the spinal components) in order to gain insight into the overall performance of the proposed CAD system. We consider the following three parameters: (1) TP (true positive) is the overlap between the region segmented by the algorithm and the ground truth; (2) FN (false negative) is the ground truth region which is not included by the region segmented by the algorithm.; and (3) FP (false positive) is the region segmented by the algorithm but is not found in the ground truth. The correlation between the three regions is illustrated in Figure 14. Based on these parameters, we can define the performance metric, Quality of the segmentation result, as: (1) Similarly, Diagnosis Correctness is computed as the percentage of correct diagnosis. In our experiments, we performed a ten-fold cross validation [9] for each performance metric as follows. The data set of 50 subjects is randomly split into ten approximately equal partitions. Each partition is used in turn for testing while the remaining partitions are used for training. This process is repeated ten times; and the overall performance is the average over the ten rounds. This assessment strategy reduces the variability and gives more accurate performance results.

Figure 14. Performance parameters. 4.2 Correctness of the Segmentation In our experiment, the MLP for image segmentation, as illustrated in Figure 9, was implemented with a learning rate of 0.3, momentum of 0.2, training time of 500, validation threshold of 20, and 16 nodes in the hidden layer. The performance results of the spinal component segmentation as described in Section 2.1 are given in Table 3. Overall, the accuracy consistently ranges above 91%, which is adequate for our diagnosis purposes. Table 3. The average percentage of correct segmentation of spinal components Spinal Components Segmentation Quality (1) Spinal canal 92.47 (2.1) Intervertebral disc 91.47 (2.2) Right superior articular facet 93.33 (2.3) Left superior articular facet 91.25 (3) Right ligamentum flavum & facet 97.15 (4) Left ligamentum flavum & facet 98.21 4.2 Quality of the Spinal stenosis classification After the spinal components are segmented, spinal features such as the anteroposterior diameter, lateral canal distance, and ligamentum flavum thickness in each image are calculated and extracted. These features are input into the Multilayer Perceptrons, as illustrated in Figure 12, to predict the stenosis conditions. The MLP s for diagnosis was implemented with a learning rate of 0.3, momentum of 0.2, training time of 500, validation threshold of 20, and 10 nodes in the hidden layer. Again, the ten-fold cross validation is applied in this study. The results are shown in Table 4. Table 4. The average percentage of correct diagnosis Spinal conditions Diagnosis Correctness Hypertrophy of ligament flavum & facet 96.82 Disc Herniation 92.31 Central Spinal stenosis 92.66 Lateral Spinal stenosis 96.29

In this study, we do not include intervertebral disc level L5-S1 in computing the correctness of disc herniation diagnosis since L5-S1 looks quite different from the other disc levels. The results in Table 4 indicate that MRI is significantly more effective in facilitating spinal stenosis diagnosis, compared to our own older X-ray CAD system whose correctness averages 74.5%. This can be attributed to the fact that MR images can reveal more important spinal features and enhance boundaries of spinal components allowing the segmentation and diagnosis classification to be more accurate. 6. CONCLUSIONS AND FUTURE RESEARCH We have developed a fully automatic computer-aided diagnosis system for lumbar spine stenosis. The system accepts a series of T2-weighted axial views as input, and detects the following stenosis conditions: hypertrophy of ligament flavum and facet, disc herniation, central spinal stenosis, and lateral spinal stenosis. The average accuracy ranges from about 92% to 97%. The performance can be further improved by considering also the sagittal views (Figure 3(B)) of lumbar spine. There are several spinal conditions, related to spinal stenosis, such as posterior osteophyte, disc space narrowing, and spondylolisthesis that were not considered directly in this study. These spinal conditions could be recognized from the sagittal views. We will extend our CAD system with these new capabilities in our future research. REFERENCES [1] Abrahams, P., [The atlas of the human body], Bright Star Publishing plc.,london, 86-93 (2008). [2] Agency of Healthcare Research and Quality, Treatment of Degenerative Lumbar Spinal Stenosis, AHRQ Publication, (2001). [3] Bogduk, N., [Clinical anatomy of the lumbar spine and sacrum], Churchill Livingstone Publishers, London, Third edition,187-214 (2005). [4] Furman, M. B. and Puttlitz, K. M., Spinal Stenosis and Neurogenic Claudication, EMedicine (2009). (http://www.emedicine.com/pmr/topic133.htm). [5] Gunzburg, S.M. and Hagerstown, R., Lumbar spinal stenosis. Lippincott Williams & Wilkins. 1999. [6] Haykin, S. Neural Networks: A Comprehensive Foundation (2 ed.). Prentice Hall, (1998). [7] Hudgins, W.R., Computer-aided diagnosis of lumbar disk herniation, Spine 8, 604-615 (1983). [8] Hedlund, L. R. and Gallagher, J. C., Vetebral morphometry in diagnosis of spinal fractures, Bone Miner, 59-67 (1988). [9] Kohavi, R., A study of cross validation and bootstrap for accuracy and model selection, Proc. AI 2(12),1137-1143 (1995). [10] Koompairojn, S., Hua, K. A. and Bhadrakom, C., Automatic classification system for lumbar spine X-ray images, Proc. CBMS 1(1), 213 218 (2006). [11] National Institute of Neurological Disorders and Stroke (NINDS), Low back pain fact,, NINDS Publication, (2008).(http://www.ninds.nih.gov/disorders/backpain/). [12] Seewald, A., Introduction to Weka, (2009). (http://www.cs.waikato.ac.nz/ml/weka/). [13] Sennst, D.A., Kachelriess, M., Leidecker, C., Schmidt, B., Watzke, O. and Kalender,W.A., An extensible software-based platform for reconstruction and evaluation of CT images, RadioGraphics 24, 601-603 (2004). [14] Seung, S., Multilayer perceptrons and backpropagation learning, Lecture Notes (2002). [15] Stegmann, M. B., Active Appearance Model, theory extensions and cases, Master Thesis,Technical University of Denmark (2000). [16] Wasserman, P.D.; Schwartz, T., Neural networks. II. What are they and why is everybody so interested in them now?, IEEE Expert 3(1),10-15; (1988).