Shape modeling and variability mapping in human brain structures
Abstract/Contents
- Abstract
- This thesis is about modeling and understanding the variability in shape of different brain structures and how these variabilities relate to function, health and disease. As the capabilities of imaging methods increase in resolution and accuracy, worldwide efforts are now focused on acquiring neuroimaging data of large human populations, both normal and abnormal, and making this data available to the scientific community. The data from these massive datasets is processed to extract three-dimensional representations of the different structures in the human brain in-vivo. At present, the neuroscientific community lacks the geometric language to analyze and model shape variations in these structures across populations. While statistical atlases of average brain shapes provide an understanding of what is accepted as 'normal' and 'abnormal', more precise modeling methods are needed for better quantification and understanding. The goal of this thesis is to equip the neuroanatomist with a precise language for assessing neuroanatomical geometric variations. To this end we describe the following contributions: (1) Framework for classification of different stages of Alzheimer's disease based solely on the geometry of an ensemble of brain structures. Our approach uses statistical learning, achieves high classification accuracy and is sensitive to identifying the onset of Alzheimer's disease; (2) Training a neural network to classify Alzheimer's disease based on structural (T1-MRI) and functional (PET) imaging data; (3) Two different geometric models to capture the shape of white matter fiber bundles. We show several applications of these models, including fiber bundle registration, shape atlas and shape differences measure and derive a normative model for white matter development in the pediatric brain.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Glozman, Tatiana |
|---|---|
| Degree supervisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Horowitz, Mark (Mark Alan) |
| Thesis advisor | Wandell, Brian A |
| Degree committee member | Horowitz, Mark (Mark Alan) |
| Degree committee member | Wandell, Brian A |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Tanya Glozman. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Tatiana Glozman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...