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Computer Aided Diagnosis System for Lumbar Spinal Stenosis Using X-ray Images. Soontharee Koompairojn Kien A. Hua School of EECS University of Central Florida. Chutima Bhadrakom Department of Radiology Thai Nakarin Hospital Thailand. Outline. Background Methodology

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Computer Aided Diagnosis System for Lumbar Spinal Stenosis Using X-ray Images

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    1. Computer Aided Diagnosis System for Lumbar Spinal Stenosis Using X-ray Images Soontharee Koompairojn Kien A. Hua School of EECS University of Central Florida ChutimaBhadrakom Department of Radiology Thai Nakarin Hospital Thailand

    2. Outline • Background • Methodology • Classifiers Construction • Automatic diagnosis • Prototype • Experimental Studies • Conclusions

    3. Our Back Spine is made up of a series of vertebrae (bone) and disks (elastic tissue) Spine

    4. Facet Joints • A joint is where two or more bones are joined • Joints allow motion • The joins in the spine are called Facet Joints • Each vertebra has two set of facet joints. One pair faces upward and one downward • Facet joints are hinge-like and link vertebrae together

    5. Spine Anatomy First three sections of the spine: • Cervical Spine: Neck – C1 through C7 • Thoracic Spine: Upper and mid back – T1 through T12 • Lumbar Spine: Lower back - L1 through L5

    6. Spinal Cord • Each vertebra has a hole through it • These holes line up to form the spinal canal • A large bundle of nerves called the spinal cordruns through the spinal canal Jelly-like nucleus Holes line up Tough outer shell Hole

    7. Spinal Nerves • Spinal cord has 31 segments; and a pair of spinal nerves exits from each segment • These nerves carry messages between the brain and the various parts of the body

    8. Link between Brain & Body Each segment of the spinal cord controls different parts of the body

    9. Spinal Cord is Shorter • Spinal cord is much shorter than the length of the spinal column • Spinal cord extends down to only the last of the thoracic vertebrae • Nerves that branch from the spinal cord from the lumbar level must run in the vertebral canal for a distance before they exit the vertebral column

    10. Shape & Size of Spinal Segments • Nerve cell bodies are located in the “gray” matter • Axons of the spinal cord are located in the “white” matter. They carry messages. • Spinal segments closer to the brain have larger amount of “white” matter • Because many axons go up to the brain from all levels of the spinal cord More “white” matter

    11. Spinal Stenosis • Spinal stenosis is a progressive narrowing of the opening in the spinal canal, which places pressure on the spinal cord (nerve roots) • Pressure on nerve roots causes • chronic pain, and • loss of control over some functions because communication with the brain is interrupted

    12. Spinal Stenosis • Cervical spinal stenosis: Stenosis (narrowing) is located in the neck • Lumbar Spinal Stenosis: Stenosis is located on the lower part of the spinal cord • 75% of cases of spinal stenosis occur in the low back (lumbar spine), and legs are affected • Produce pain in the legs with walking, and the pain is relieved with sitting

    13. We focus on Lumbar Spine Stenosis

    14. Diagnosis • Patients with lumbar spinal stenosis may feel pain, weekness, or numbness in the legs, calves or buttocks • Other conditions can cause similar symptoms • Spinal tumors • Disorders of the blood flow (circulatory disorders) • Spinal stenosis diagnosis is not easy

    15. We Try to Detect These Conditions • Disc Space Narrowing • Abnormal Bony Growth (Posterior osteophytes) • Abnormality of FacetJoint(Posterior ApophysealArthropathy) • Vertibral Slippage (Spondylolisthesis)

    16. Disc Space Narrowing • As the spine gets older, the discs lose height as the materials in them dries out and shrinks • Causing the middle part of vertebrae to push down resulting in bulging discs and herinated discs • Bulging discs and herinated discs encroach into the canal to narrow it and hence producing stenosis

    17. Posterior Apophyseal Arthropathy (abnormality of facet joint) • Disc space narrowing can also cause instability between vertebrae • The body attempts to reduce the instability by trying to fuse around the bad disc • The facet joints enlarge and the edges try to fuse together and hence producing stenosis

    18. Osteophytes(abnormal bony outgrowth) • Osteophyte - Small abnormal bony outgrowth (bone spurs) • Anterior Osteophyte - Outgrowth at the front side of a vertebrae • Posterior Osteophyte - Outgrowth in the back side of a vertebrae

    19. Spondylolisthesis A Vertebra is slipping off another

    20. Summary • Disc Space Narrowing – bulging and herinated discs • Posterior osteophytes – bone spurs • Posterior Apophyseal Arthropathy – abnormal growth on facet joints • Spondylolisthesis – vertebral slippage We detect these conditions using X ray

    21. Motivation • Prior studies need manually determined boundary for each individual vertebra • No computer-aided diagnosis (CAD) system for spinal stenosis • Develop a fully automatic CAD for spinal stenosis • Focus on X-rays as this is often the first test for spinal stenosis diagnosis

    22. Imaging Technology • X-RAYS: These show (1) disc narrowing, (2) bone spurs (osteophytes), and (3) vertebrae slipping off another (spondylo-listhesis) • CAT SCAN: This is a computerized X ray that shows how much the diameter of the canal is reduced and how far out the discs are • M.R.I. (Magnetic Resonance Imaging): It produces picture like the CAT scan but they are generated using a magnetic field (instead of radiation) – not needed if the CAT scan shows the problems.

    23. I I,J: Anteroposterior (A-P) width of unusual spinal canal D,E,F: Intervertebral disc space height D E J F H A C G,H: Anteroposterior (A-P) width of usual spinal canal B G A: Anterior vertebral height C: Posterior vertebral height B: Mid vertebral height Features

    24. Feature Extraction • Automatically determine the boundary points • Using the Active Appearance Model (AAM) technique • Measure the distances among the boundary points to extract the features Boundary point

    25. Active Appearance Model(morphable model) • An AAM contains a statistical model of the appearance of the object of interest (e.g., face) which can generalize to almost any valid example • The AAM can search for the structures from a displaced initial position Initial position After 1 iteration After 2 iteration Convergence Face model Built from 400 images

    26. Apply AAM to our Environment • A radiologist manually labels boundary points of training images • Apply the AAM technique to build a lumbar model (with boundary points) • Apply the lumbar model to determine the boundary points of the image under investigation • Measure the distances among the boundary points to obtain the feature values

    27. Spine X-ray image

    28. Result from AAM posterior osteophyte (bone spur) apophyseal arthopathy (growth on facet joint) spondylolisthesis (vertebral slippage)

    29. Predicting spinal conditions • Bayesian framework is used to build a classifier for each spinal condition • Choosing the most probable spinal condition given extracted features xi : Extracted features Ci : Spinal condition i P : Posterior probability for each spinal condition P* : Highest posterior probability If P* > threshold  spinal stenosis

    30. Naïve Bayes Classifier (1) • Prior Probability: Prior probabilities are based on previous experience

    31. Naïve Bayes Classifier (2) • Likelihood: Likelyhood of X given Red/Green X

    32. Naïve Bayes Classifier (3) Posterior Probability: combining the prior and the likelihood to form a posterior probability using Bayes’ rule X Percentage of Green in the neighborhood Percentage of Green population

    33. Naïve Bayes Classifier (4) We classify X as RED

    34. Multiple Independent Variables • Posterior probability for the event Cj among a set of possible outcomes C = {C1, C2, …, Cd) Likelihood Posterior probability of class membership, i.e., the probability that X belongs to Cj Conditional probability of independent Variables are statistically independent Likelihood

    35. Multiple Independent Variables • Probability that X belongs to Cj • Using Bayes’ rule above, we label a new case X with a class level Cmthat achieves the highest posterior probability  X belongs to Cm

    36. Automatic Stenosis Diagnosis • Probability that X belongs to Cj • Using Bayes’ rule above, we diagnose a new case X as follows: If p(Cm|X) > threshold  spinal stenosis

    37. New X-ray case Training interface User interface Training & learning process X-ray training cases Image segmentation Feature Extraction Result Feature Extraction Classifier Classification Feature Vectors System Architecture Classifiers construction Automatic diagnosis

    38. GUI for Classifier Construction The user interface for managing training images and building lumbar spine classifiers

    39. GUI for Stenosis Diagnosis The user interface for submitting X-ray images for analysis of spinal conditions

    40. Data Set for Experiments • 86 lumbar spine X-ray images from NHANES II • database • 70 cases for training 16 cases for testing There are 17,000 spine X-ray images in the NHANES II database collected by the second National Health and Nutrition Examination Survey

    41. Average Percentage of correct prediction of training images

    42. Average Percentage of Correct Prediction of test images

    43. Average Percentage of correct prediction using perfect labels Better labeling improves performance

    44. Conclusions • A fully automatic CAD system for lumbar spinal stenosis • Not dependent on user’s knowledge and experience • Accuracy from 75 – 80% • Good enough for screening and initial diagnosis • Suitable for general practitioners

    45. Do You Know ? • Giraffes and human have SEVEN vertebrae in their necks