Abstract
Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient's lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient's lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm (p = 0.92) and 0.29 mm (p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm (p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.
Highlights
Lumbar Spinal Stenosis (LSS) causes low back pain through pressures exerted on the spinal nerve and could result in sciatica which symptoms include radicular pain, atypical leg pain, and neurogenic claudication [1]
The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine Magnetic Resonance Imaging (MRI) dataset containing MRI studies of 515 patients
We experimented with several combinations of α, β, and γ parameter values when implementing the geodesic active contour (GAC) method to produce an acceptable compromise between accuracy and smoothness, and we found that α = 1, β = 160, and γ = 500 are the best combination to use
Summary
Lumbar Spinal Stenosis (LSS) causes low back pain through pressures exerted on the spinal nerve and could result in sciatica which symptoms include radicular pain, atypical leg pain, and neurogenic claudication [1]. The Royal College of Radiologists reported [4] that three-quarters of UK medical imaging departments do not have sufficient radiology consultants to deliver safe and effective patient care, and subsequently, more money is being spent to pay outsourcing, overtime, and locums to cover radiologist work every year This problem is exacerbated by the fact that there is a 12.3% average annual growth in demand for radiographical imaging including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans since 1995 [5]. These roots exit the spinal cord and cauda equina through the left and right openings between vertebrae as spinal nerves and caudal nerve roots, respectively.
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