Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET
Despite the association between hippocampal atrophy and a vast array of highly debilitating neurological diseases, such as Alzheimer’s disease and frontotemporal lobar degeneration, tools to accurately and robustly quantify the degeneration of this structure still largely elude us. In this thesis, w...
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| Format: | Thesis |
| Language: | English |
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2012
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| _version_ | 1817932279452794880 |
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| author | Bishop, CA |
| author2 | Jenkinson, M |
| author_facet | Jenkinson, M Bishop, CA |
| author_sort | Bishop, CA |
| collection | OXFORD |
| description | Despite the association between hippocampal atrophy and a vast array of highly debilitating neurological diseases, such as Alzheimer’s disease and frontotemporal lobar degeneration, tools to accurately and robustly quantify the degeneration of this structure still largely elude us. In this thesis, we firstly evaluate previously-developed hippocampal segmentation methods (FMRIB’s Integrated Registration and Segmentation Tool (FIRST), Freesurfer (FS), and three versions of a Classifier Fusion (CF) technique) on two clinical MR datasets, to gain a better understanding of the modes of success and failure of these techniques, and to use this acquired knowledge for subsequent method improvement (e.g., FIRSTv3). Secondly, a fully automated, novel hippocampal segmentation method is developed, termed Fast Marching for Automated Segmentation of the Hippocampus (FMASH). This combined region-growing and atlas-based approach uses a 3D Sethian Fast Marching (FM) technique to propagate a hippocampal region from an automatically-defined seed point in the MR image. Region growth is dictated by both subject-specific intensity features and a probabilistic shape prior (or atlas). Following method development, FMASH is thoroughly validated on an independent clinical dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with an investigation of the dependency of such atlas-based approaches on their prior information. In response to our findings, we subsequently present a novel label-warping approach to effectively account for the detrimental effects of using cross-dataset priors in atlas-based segmentation. Finally, a clinical application of MR hippocampal segmentation is presented, with a combined MR-PET analysis of wholefield and subfield hippocampal changes in Alzheimer’s disease and frontotemporal lobar degeneration. This thesis therefore contributes both novel computational tools and valuable knowledge for further neurological investigations in both the academic and the clinical field. |
| first_indexed | 2024-03-07T07:28:27Z |
| format | Thesis |
| id | oxford-uuid:2549bad2-432f-4d0e-8878-be9cce6ae0d2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:35:23Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:2549bad2-432f-4d0e-8878-be9cce6ae0d22024-12-01T18:57:57ZDevelopment and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PETThesishttp://purl.org/coar/resource_type/c_db06uuid:2549bad2-432f-4d0e-8878-be9cce6ae0d2MetabolismBipolar disorderMathematical modeling (engineering)DementiaComputational NeuroscienceImage understandingNumerical analysisMemoryApplications and algorithmsNeurologyNeuropathologyProgram development and toolsEnglishOxford University Research Archive - Valet2012Bishop, CAJenkinson, MDeclerck, JMerhof, DDespite the association between hippocampal atrophy and a vast array of highly debilitating neurological diseases, such as Alzheimer’s disease and frontotemporal lobar degeneration, tools to accurately and robustly quantify the degeneration of this structure still largely elude us. In this thesis, we firstly evaluate previously-developed hippocampal segmentation methods (FMRIB’s Integrated Registration and Segmentation Tool (FIRST), Freesurfer (FS), and three versions of a Classifier Fusion (CF) technique) on two clinical MR datasets, to gain a better understanding of the modes of success and failure of these techniques, and to use this acquired knowledge for subsequent method improvement (e.g., FIRSTv3). Secondly, a fully automated, novel hippocampal segmentation method is developed, termed Fast Marching for Automated Segmentation of the Hippocampus (FMASH). This combined region-growing and atlas-based approach uses a 3D Sethian Fast Marching (FM) technique to propagate a hippocampal region from an automatically-defined seed point in the MR image. Region growth is dictated by both subject-specific intensity features and a probabilistic shape prior (or atlas). Following method development, FMASH is thoroughly validated on an independent clinical dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with an investigation of the dependency of such atlas-based approaches on their prior information. In response to our findings, we subsequently present a novel label-warping approach to effectively account for the detrimental effects of using cross-dataset priors in atlas-based segmentation. Finally, a clinical application of MR hippocampal segmentation is presented, with a combined MR-PET analysis of wholefield and subfield hippocampal changes in Alzheimer’s disease and frontotemporal lobar degeneration. This thesis therefore contributes both novel computational tools and valuable knowledge for further neurological investigations in both the academic and the clinical field. |
| spellingShingle | Metabolism Bipolar disorder Mathematical modeling (engineering) Dementia Computational Neuroscience Image understanding Numerical analysis Memory Applications and algorithms Neurology Neuropathology Program development and tools Bishop, CA Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title | Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title_full | Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title_fullStr | Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title_full_unstemmed | Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title_short | Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET |
| title_sort | development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using mri and pet |
| topic | Metabolism Bipolar disorder Mathematical modeling (engineering) Dementia Computational Neuroscience Image understanding Numerical analysis Memory Applications and algorithms Neurology Neuropathology Program development and tools |
| work_keys_str_mv | AT bishopca developmentandapplicationofimageanalysistechniquestostudystructuralandmetabolicneurodegenerationinthehumanhippocampususingmriandpet |