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 Chutima Bhadrakom Department of Radiology Thai Nakarin Hospital Thailand

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

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

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 4

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 5

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 cord runs through the spinal canal 6 Hole Holes line up Tough outer shell Jelly-like nucleus

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 7

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

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 9

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 10 More “white” matter

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 11  chronic pain, and  loss of control over some functions because communication with the brain is interrupted

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 12

We focus on Lumbar Spine Stenosis 13

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 14

We Try to Detect These Conditions  Disc Space Narrowing  Abnormal Bony Growth (Posterior osteophytes)  Abnormality of FacetJoint (Posterior Apophyseal Arthropathy)  Vertibral Slippage (Spondylolisthesis) 15

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 16

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 17

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 18

Spondylolisthesis A Vertebra is slipping off another 19

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

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 21

Imaging Technology 1. X-RAYS: These show (1) disc narrowing, (2) bone spurs (osteophytes), and (3) vertebrae slipping off another (spondylo-listhesis) 2. 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 3. 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. 22

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

Extracting feature 24 A: Anterior vertebral height B: Mid vertebral height C: Posterior vertebral height D: Anterior height of intervertebral disc space E: Mid height of intervertebral disc space F: Postrior height of intervertebral disc space G: Upper anteroposterior (A-P) width of usual spinal canal H: Lower anteroposterior (A-P) width of usual spinal canal I: Upper anteroposterior (A-P) width of unusual spinal canal J: Lower anteroposterior (A-P) width of unusual spinal canal When a vertebra is normal, some of the boundary points near the canal are at the same location (e.g., points 4 & 11 vs. point 1)

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 25 Boundary point

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 26 Initial position After 1 iteration After 2 iteration Convergence Face model Built from 400 images

Apply AAM to our Environment 1. A radiologist manually labels boundary points of training images 2. Apply the AAM technique to build a lumbar model (with boundary points) 3. Apply the lumbar model to determine the boundary points of the image under investigation 4. 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) 29 spondylolisthesis (vertebral slippage)

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

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 32 X

Naïve Bayes Classifier (3) Posterior Probability: combining the prior and the likelihood to form a posterior probability using Bayes’ rule 33 Percentage of Green population Percentage of Green in the neighborhood X

Naïve Bayes Classifier (4) 34 We classify X as RED

Multiple Independent Variables Posterior probability for the event Cj among a set of possible outcomes C = {C1, C2, …, Cd) 35 Posterior probability of class membership, i.e., the probability that X belongs to Cj Likelihood Conditional probability of independent Variables are statistically independent Likelihood

Multiple Independent Variables Probability that X belongs to Cj Using Bayes’ rule above, we label a new case X with a class level C m that achieves the highest posterior probability 36  X belongs to C m

Automatic Stenosis Diagnosis Probability that X belongs to Cj Using Bayes’ rule above, we diagnose a new case X as follows: 37 If p(C m |X) > threshold  spinal stenosis

System Architecture 38 Feature Extraction Training & learning process Feature Vectors Training interface User interface Image segmentation Classification Feature Extraction Result X-ray training cases New X-ray case Classifier Classifiers construction Automatic diagnosis

GUI for Classifier Construction 39 The user interface for managing training images and building lumbar spine classifiers

GUI for Stenosis Diagnosis 40 The user interface for submitting X-ray images for analysis of spinal conditions

Data Set for Experiments 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 Spinal Conditions Intervertebral Disc Level L2-L3L3-L4L4-L5Total Posterior Osteophyte Posterior Apophyseal Arthorphathy Disc Space Narrowing Spondylooisthesis 1012 Spinal Stenosis

Average Percentage of correct prediction of training images 42 Spinal Conditions Intervertebral Disc Level L2-L3L3-L4L4-L5Total Posterior Osteophyte Posterior Apophyseal Arthorphathy Disc Space Narrowing Spondylooisthesis Spinal Stenosis

Average Percentage of Correct Prediction of test images 43 Spinal Conditions Intervertebral Disc Level L2-L3L3-L4L4-L5Total Posterior Osteophyte Posterior Apophyseal Arthorphathy Disc Space Narrowing Spondylooisthesis Spinal Stenosis

Average Percentage of correct prediction using perfect labels 44 Better labeling improves performance Spinal Conditions Intervertebral Disc Level L2-L3L3-L4L4-L5Total Posterior Osteophyte Posterior Apophyseal Arthorphathy Disc Space Narrowing Spondylooisthesis Spinal Stenosis

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 46