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postgraduate thesis: An automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord

TitleAn automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord
Authors
Advisors
Advisor(s):Cheung, JPYHu, Y
Issue Date2018
PublisherThe 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.
AbstractObjective: 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.
DegreeMaster of Philosophy
SubjectCervical vertebrae|xMagnetic resonance imaging
Diffusion tensor imaging
Dept/ProgramOrthopaedics and Traumatology
Persistent Identifierhttp://hdl.handle.net/10722/266338

 

DC FieldValueLanguage
dc.contributor.advisorCheung, JPY-
dc.contributor.advisorHu, Y-
dc.contributor.authorChan, Tin-yan-
dc.contributor.author陳天恩-
dc.date.accessioned2019-01-18T01:52:06Z-
dc.date.available2019-01-18T01:52:06Z-
dc.date.issued2018-
dc.identifier.citationChan, 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.urihttp://hdl.handle.net/10722/266338-
dc.description.abstractObjective: 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.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshCervical vertebrae|xMagnetic resonance imaging-
dc.subject.lcshDiffusion tensor imaging-
dc.titleAn automated region of interest (ROI) segmentation tool on diffusion tensor imaging of the cervical spinal cord-
dc.typePG_Thesis-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineOrthopaedics and Traumatology-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_991044069404603414-
dc.date.hkucongregation2018-
dc.identifier.mmsid991044069404603414-

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