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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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