Fast Automatic Segmentation of Thalamic Nuclei
| dc.contributor.advisor | Bilgin, Ali | en |
| dc.contributor.advisor | Saranathan, Manojkumar | en |
| dc.contributor.author | Thomas, Francis Tyson | |
| dc.creator | Thomas, Francis Tyson | en |
| dc.date.accessioned | 2018-01-23T22:43:25Z | |
| dc.date.available | 2018-01-23T22:43:25Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://hdl.handle.net/10150/626390 | |
| dc.description.abstract | Fast, automated segmentation of the thalamic nuclei in the brain has long been desired as it provides for direct visualization of the target for certain procedures like Deep Brain Stimulation (DBS) that target a specific nucleus. It is also beneficial in the study of other pathologies that pertain to different nuclei. In this thesis, a novel approach to fast automated segmentation of thalamic nuclei called Shortened Template and THalamus for Optimal Multi Atlas Segmentation (ST THOMAS) was developed using the multi-atlas segmentation approach. It was designed with a focus on robustness and speed by making use of an averaged template for registration and cropping the inputs and the template. The performance of ST THOMAS was first evaluated on 7T MRI data by comparing with manual delineation (ground truth) by an expert neuroradiologist. Dice coefficients and Volumetric Similarity Indices were used as metrics. To extend the applicability of this method, 3T MRI data were also evaluated. Finally, applications to real time ventralintermideiate (VIM) nucleus targeting for DBS and study of the effects of alcoholism are demonstrated. | |
| dc.language.iso | en_US | en |
| dc.publisher | The University of Arizona. | en |
| dc.rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. | en |
| dc.subject | Real Time | en |
| dc.subject | Segmentation | en |
| dc.subject | Thalamic Nuclei | en |
| dc.subject | Thalmus | en |
| dc.title | Fast Automatic Segmentation of Thalamic Nuclei | en_US |
| dc.type | text | en |
| dc.type | Electronic Thesis | en |
| thesis.degree.grantor | University of Arizona | en |
| thesis.degree.level | masters | en |
| dc.contributor.committeemember | Bilgin, Ali | en |
| dc.contributor.committeemember | Saranathan, Manojkumar | en |
| dc.contributor.committeemember | Lysecky, Roman | en |
| thesis.degree.discipline | Graduate College | en |
| thesis.degree.discipline | Electrical & Computer Engineering | en |
| thesis.degree.name | M.S. | en |
| refterms.dateFOA | 2018-09-12T01:04:37Z | |
| html.description.abstract | Fast, automated segmentation of the thalamic nuclei in the brain has long been desired as it provides for direct visualization of the target for certain procedures like Deep Brain Stimulation (DBS) that target a specific nucleus. It is also beneficial in the study of other pathologies that pertain to different nuclei. In this thesis, a novel approach to fast automated segmentation of thalamic nuclei called Shortened Template and THalamus for Optimal Multi Atlas Segmentation (ST THOMAS) was developed using the multi-atlas segmentation approach. It was designed with a focus on robustness and speed by making use of an averaged template for registration and cropping the inputs and the template. The performance of ST THOMAS was first evaluated on 7T MRI data by comparing with manual delineation (ground truth) by an expert neuroradiologist. Dice coefficients and Volumetric Similarity Indices were used as metrics. To extend the applicability of this method, 3T MRI data were also evaluated. Finally, applications to real time ventralintermideiate (VIM) nucleus targeting for DBS and study of the effects of alcoholism are demonstrated. |
