Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network

Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network

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 a...

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Main Authors: Al Kafri, Ala S, Sudirman, Sud, Hussain, Abir Jaafar, Al-Jumeily, Dhiya A., Fergus, Paul, Natalia, Friska, Meidia, Hira, Afriliana, Nunik, Sophian, Ali, Al-Jumaily, Mohammed, Al-Rashdan, Wasfi, Bashtawi, Mohammad
Format: Conference or Workshop Item
Language:English
English
English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:
QA76 Computer software
QP Physiology
RD Surgery
T Technology (General)
Online Access:http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/1/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_cover%20page.png
http://irep.iium.edu.my/69097/2/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_schedule.pdf
http://irep.iium.edu.my/69097/3/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_conf.%20article.pdf
http://irep.iium.edu.my/69097/4/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_scopus.pdf
id iium-69097
recordtype eprints
spelling iium-690972018-12-31T08:00:48Z http://irep.iium.edu.my/69097/ Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network Al Kafri, Ala S Sudirman, Sud Hussain, Abir Jaafar Al-Jumeily, Dhiya A. Fergus, Paul Natalia, Friska Meidia, Hira Afriliana, Nunik Sophian, Ali Al-Jumaily, Mohammed Al-Rashdan, Wasfi Bashtawi, Mohammad QA76 Computer software QP Physiology RD Surgery T Technology (General) 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 patchbased 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. Institute of Electrical and Electronics Engineers Inc. 2018-09-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/69097/1/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_cover%20page.png application/pdf en http://irep.iium.edu.my/69097/2/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_schedule.pdf application/pdf en http://irep.iium.edu.my/69097/3/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_conf.%20article.pdf application/pdf en http://irep.iium.edu.my/69097/4/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_scopus.pdf Al Kafri, Ala S and Sudirman, Sud and Hussain, Abir Jaafar and Al-Jumeily, Dhiya A. and Fergus, Paul and Natalia, Friska and Meidia, Hira and Afriliana, Nunik and Sophian, Ali and Al-Jumaily, Mohammed and Al-Rashdan, Wasfi and Bashtawi, Mohammad (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 2018, 8 July - 13 July 2018, Rio de Janeiro; Brazil. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8477893 10.1109/CEC.2018.8477893
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
English
English
topic QA76 Computer software
QP Physiology
RD Surgery
T Technology (General)
spellingShingle QA76 Computer software
QP Physiology
RD Surgery
T Technology (General)
Al Kafri, Ala S
Sudirman, Sud
Hussain, Abir Jaafar
Al-Jumeily, Dhiya A.
Fergus, Paul
Natalia, Friska
Meidia, Hira
Afriliana, Nunik
Sophian, Ali
Al-Jumaily, Mohammed
Al-Rashdan, Wasfi
Bashtawi, Mohammad
Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
description 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 patchbased 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.
format Conference or Workshop Item
author Al Kafri, Ala S
Sudirman, Sud
Hussain, Abir Jaafar
Al-Jumeily, Dhiya A.
Fergus, Paul
Natalia, Friska
Meidia, Hira
Afriliana, Nunik
Sophian, Ali
Al-Jumaily, Mohammed
Al-Rashdan, Wasfi
Bashtawi, Mohammad
author_facet Al Kafri, Ala S
Sudirman, Sud
Hussain, Abir Jaafar
Al-Jumeily, Dhiya A.
Fergus, Paul
Natalia, Friska
Meidia, Hira
Afriliana, Nunik
Sophian, Ali
Al-Jumaily, Mohammed
Al-Rashdan, Wasfi
Bashtawi, Mohammad
author_sort Al Kafri, Ala S
title Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
title_short Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
title_full Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
title_fullStr Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
title_full_unstemmed Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network
title_sort segmentation of lumbar spine mri images for stenosis detection using patch-based pixel classification neural network
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2018
url http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/
http://irep.iium.edu.my/69097/1/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_cover%20page.png
http://irep.iium.edu.my/69097/2/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_schedule.pdf
http://irep.iium.edu.my/69097/3/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_conf.%20article.pdf
http://irep.iium.edu.my/69097/4/69097_Segmentation%20of%20Lumbar%20Spine%20MRI%20Images%20for%20Stenosis%20Detection_scopus.pdf
first_indexed 2023-09-18T21:38:04Z
last_indexed 2023-09-18T21:38:04Z
_version_ 1777412954607583232

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