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.
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. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. 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. 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. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. 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. 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. 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. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves.
#Spine2d matlab code#
MATLAB source code for automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosisĭata Archiving and Networked Services (DANS)
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