Automated radiological analysis of spinal MRI
<p>This thesis addresses the problem of analysing clinical MRI using modern computer vision methods for a variety of clinical and research-related tasks. We use automated machine learning algorithms to develop a spinal MRI analysis framework for a number of tasks such as vertebrae detection, l...
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| Format: | Thesis |
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2015
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| _version_ | 1797069854477385728 |
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| author | Lootus, M |
| author2 | Zisserman, A |
| author_facet | Zisserman, A Lootus, M |
| author_sort | Lootus, M |
| collection | OXFORD |
| description | <p>This thesis addresses the problem of analysing clinical MRI using modern computer vision methods for a variety of clinical and research-related tasks. We use automated machine learning algorithms to develop a spinal MRI analysis framework for a number of tasks such as vertebrae detection, labelling; disc and vertebrae segmentation, and radiological grading, and we validate the framework on a large, heterogeneous dataset of 300 symptomatic back pain patients from multiple clinical sites and scanners. Our framework has a number of back pain research and other spine-related clinical applications and could hopefully find application in a clinical workflow in the future.</p> <p>Our framework has five steps -- detection, labelling, segmentation, support regions and features, and machine learning for radiological measurements. The framework works in full 3D and has currently been implemented on sagittal T2 slices. We use Deformable Part Models along with a chain model to detect and label vertebrae, and a powerful graph cuts based method for vertebrae and disc segmentation. The labelled detections and segmentations are used to place support regions for feature extraction, which are mapped into a number of radiological measurements -- namely Pfirrmann grade, disc space narrowing, and herniation/bulge. The radiological ground truth was provided by a clinical radiologist with 25 years experience. We demonstrate a high performance in the measurement in each. The measurements are performed using support vector machines and support vector regressors learned on training data.</p> <p>We next investigate the problem of what is the best method of obtaining support regions. We first used pixel intensity features to predict the Pfirrmann grade, narrowing and bulge/herniation, with vertebrae segmentation to localise their support regions. Since segmentation of spine images, especially intervertebral discs is an unsolved problem and algorithms are prone to failure, we then ask the question, to segment or not to segment. To answer the question, we compare results on Pfirrmann grade prediction with three different points on the no segmentation to full disc segmentation involving no segmentation, vertebrae segmentation, or disc segmentation and find that vertebrae segmentation suffices.</p> <p>We finally show preliminary results in distinguishing between different radiological conditions related to the posterior side of the disc more finely than before in literature, taking information from both sagittal and axial slices to attempt to distinguish between herniated and bulged discs.</p> |
| first_indexed | 2024-03-06T22:30:34Z |
| format | Thesis |
| id | oxford-uuid:5820edfd-fe18-4f3c-9db3-204db75c09c2 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:30:34Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | oxford-uuid:5820edfd-fe18-4f3c-9db3-204db75c09c22022-03-26T17:01:17ZAutomated radiological analysis of spinal MRIThesishttp://purl.org/coar/resource_type/c_db06uuid:5820edfd-fe18-4f3c-9db3-204db75c09c2ORA Deposit2015Lootus, MZisserman, AKadir, T<p>This thesis addresses the problem of analysing clinical MRI using modern computer vision methods for a variety of clinical and research-related tasks. We use automated machine learning algorithms to develop a spinal MRI analysis framework for a number of tasks such as vertebrae detection, labelling; disc and vertebrae segmentation, and radiological grading, and we validate the framework on a large, heterogeneous dataset of 300 symptomatic back pain patients from multiple clinical sites and scanners. Our framework has a number of back pain research and other spine-related clinical applications and could hopefully find application in a clinical workflow in the future.</p> <p>Our framework has five steps -- detection, labelling, segmentation, support regions and features, and machine learning for radiological measurements. The framework works in full 3D and has currently been implemented on sagittal T2 slices. We use Deformable Part Models along with a chain model to detect and label vertebrae, and a powerful graph cuts based method for vertebrae and disc segmentation. The labelled detections and segmentations are used to place support regions for feature extraction, which are mapped into a number of radiological measurements -- namely Pfirrmann grade, disc space narrowing, and herniation/bulge. The radiological ground truth was provided by a clinical radiologist with 25 years experience. We demonstrate a high performance in the measurement in each. The measurements are performed using support vector machines and support vector regressors learned on training data.</p> <p>We next investigate the problem of what is the best method of obtaining support regions. We first used pixel intensity features to predict the Pfirrmann grade, narrowing and bulge/herniation, with vertebrae segmentation to localise their support regions. Since segmentation of spine images, especially intervertebral discs is an unsolved problem and algorithms are prone to failure, we then ask the question, to segment or not to segment. To answer the question, we compare results on Pfirrmann grade prediction with three different points on the no segmentation to full disc segmentation involving no segmentation, vertebrae segmentation, or disc segmentation and find that vertebrae segmentation suffices.</p> <p>We finally show preliminary results in distinguishing between different radiological conditions related to the posterior side of the disc more finely than before in literature, taking information from both sagittal and axial slices to attempt to distinguish between herniated and bulged discs.</p> |
| spellingShingle | Lootus, M Automated radiological analysis of spinal MRI |
| title | Automated radiological analysis of spinal MRI |
| title_full | Automated radiological analysis of spinal MRI |
| title_fullStr | Automated radiological analysis of spinal MRI |
| title_full_unstemmed | Automated radiological analysis of spinal MRI |
| title_short | Automated radiological analysis of spinal MRI |
| title_sort | automated radiological analysis of spinal mri |
| work_keys_str_mv | AT lootusm automatedradiologicalanalysisofspinalmri |