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Segmentation of Lumbar Spine MRI Images for Stenosis Detection using Patch-based Pixel Classification Neural Network

Al-Kafri, AS, Sudirman, S, Hussain, AJ, Al-Jumeily, D, Fergus, P, Natalia, F, Meidia, H, Afriliana, N, Sophian, A, Al-Jumaily, M, Al-Rashdan, W and Bashtawi, M (2018) Segmentation of Lumbar Spine MRI Images for Stenosis Detection using Patch-based Pixel Classification Neural Network. In: 2018 IEEE Congress on Evolutionary Computation (CEC) . (IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), 08 July 2018 - 13 July 2018, Rio de Janeiro, BRAZIL).

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Abstract

This paper addresses the central problem of automatic segmentation of lumbar spine Magnetic Resonance Imaging (MRI) images to delineate boundaries between the anterior arch and posterior arch of the lumbar spine. This is necessary to efficiently detect the occurrence of lumbar spinal stenosis as a leading cause of Chronic Lower Back Pain. A patch-based classification neural network consisting of convolutional and fully connected layers is used to classify and label pixels in MRI images. The classifier is trained using overlapping patches of size 25x25 pixels taken from a set of cropped axial-view T2-weighted MRI images of the bottom three intervertebral discs. A set of experiment is conducted to measure the performance of the classification network in segmenting the images when either all or each of the discs separately is used. Using pixel accuracy, mean accuracy, mean Intersection over Union (IoU), and frequency weighted IoU as the performance metrics we have shown that our approach produces better segmentation results than eleven other pixel classifiers. Furthermore, our experiment result also indicates that our approach produces more accurate delineation of all important boundaries and making it best suited for the subsequent stage of lumbar spinal stenosis detection.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Science & Technology; Technology; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Computer Science; Engineering; Patch-based classification neural network; lumbar spine MRI; lumbar spinal stenosis; LOW-BACK-PAIN
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science
Publisher: IEEE
Related URLs:
Date Deposited: 04 Apr 2019 08:45
Last Modified: 04 Apr 2019 08:45
DOI or Identification number: 10.1109/CEC.2018.8477893
URI: http://researchonline.ljmu.ac.uk/id/eprint/10488

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