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postgraduate thesis: An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord
Title | An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord |
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Authors | |
Advisors | |
Issue Date | 2018 |
Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
Citation | Chan, T. [陳天恩]. (2018). An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. |
Abstract | Objective: Cervical myelopathy (CM) is a chronic spinal degenerative disease that is induced by cervical spinal compression by degenerative vertebrae and disc. Diffusion Tensor Imaging (DTI) was recently studied to evaluate the pathological changes of cervical spinal cord under the affect of CM. In prior studies, researchers applied region of interest (ROI) technique to segment and analyze the DTI images in corresponding area, which is a labor intensive process and may involve biases from operators. We proposed and validated a knowledge-based automated segmentation algorithm that enables automated ROI definition on images of the DTI in cervical spinal cord in this study.
Methods: Thirty-two control subjects and eighteen patients diagnosed with CM were recruited to undergo routine Magnetic Resonance (MR) sequence and DTI for validating the knowledge-based and self-developed segmentation algorithm. Intra-observer and inter-observer reliability on the developed algorithm were evaluated.
Result: The study demonstrated the developed algorithm was fast and reliable in performing column-specific ROI definition in the control and uninvolved levels in the CM groups that enabled the possibility to conduct large scale research in health population and detect the CM involved spine levels. However, it requires further optimization to enhance the successful rate of interaction with the compressed spinal cord. |
Degree | Master of Philosophy |
Subject | Cervical vertebrae|xMagnetic resonance imaging Diffusion tensor imaging |
Dept/Program | Orthopaedics and Traumatology |
Persistent Identifier | http://hdl.handle.net/10722/266338 |
DC Field | Value | Language |
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dc.contributor.advisor | Cheung, JPY | - |
dc.contributor.advisor | Hu, Y | - |
dc.contributor.author | Chan, Tin-yan | - |
dc.contributor.author | 陳天恩 | - |
dc.date.accessioned | 2019-01-18T01:52:06Z | - |
dc.date.available | 2019-01-18T01:52:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Chan, T. [陳天恩]. (2018). An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. | - |
dc.identifier.uri | http://hdl.handle.net/10722/266338 | - |
dc.description.abstract | Objective: Cervical myelopathy (CM) is a chronic spinal degenerative disease that is induced by cervical spinal compression by degenerative vertebrae and disc. Diffusion Tensor Imaging (DTI) was recently studied to evaluate the pathological changes of cervical spinal cord under the affect of CM. In prior studies, researchers applied region of interest (ROI) technique to segment and analyze the DTI images in corresponding area, which is a labor intensive process and may involve biases from operators. We proposed and validated a knowledge-based automated segmentation algorithm that enables automated ROI definition on images of the DTI in cervical spinal cord in this study. Methods: Thirty-two control subjects and eighteen patients diagnosed with CM were recruited to undergo routine Magnetic Resonance (MR) sequence and DTI for validating the knowledge-based and self-developed segmentation algorithm. Intra-observer and inter-observer reliability on the developed algorithm were evaluated. Result: The study demonstrated the developed algorithm was fast and reliable in performing column-specific ROI definition in the control and uninvolved levels in the CM groups that enabled the possibility to conduct large scale research in health population and detect the CM involved spine levels. However, it requires further optimization to enhance the successful rate of interaction with the compressed spinal cord. | - |
dc.language | eng | - |
dc.publisher | The University of Hong Kong (Pokfulam, Hong Kong) | - |
dc.relation.ispartof | HKU Theses Online (HKUTO) | - |
dc.rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject.lcsh | Cervical vertebrae|xMagnetic resonance imaging | - |
dc.subject.lcsh | Diffusion tensor imaging | - |
dc.title | An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord | - |
dc.type | PG_Thesis | - |
dc.description.thesisname | Master of Philosophy | - |
dc.description.thesislevel | Master | - |
dc.description.thesisdiscipline | Orthopaedics and Traumatology | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.5353/th_991044069404603414 | - |
dc.date.hkucongregation | 2018 | - |
dc.identifier.mmsid | 991044069404603414 | - |