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Thesis
Development of a protocol for 3-D reconstruction of brain aneurysms from volumetric image data
University of Iowa
Master of Science (MS), University of Iowa
Summer 2010
DOI: 10.17077/etd.c8d11mr0
Abstract
<p>Cerebral aneurysm formation, growth, and rupture are active areas of investigation in the medical community. To model and test the mechanical processes involved, small aneurysm (< 5 mm) segmentations need to be performed quickly and reliably for large patient populations. In the absence of robust automatic segmentation methods, the Vascular Modeling Toolkit (VMTK) provides scripts for the complex tasks involved in computer-assisted segmentation. Though these tools give researchers a great amount of flexibility, they also make reproduction of results between investigators difficult and unreliable. We introduce a VMTK pipeline protocol that minimizes the user interaction for vessel and aneurysm segmentation and a training method for new users. This protocol allows for decision tree handling for CTA and MRA images. Furthermore, we investigate the variation between two expert users and two novice users for six patients using shape index measures developed by Ma et al. and Raghavan et al.</p>
Details
- Title: Subtitle
- Development of a protocol for 3-D reconstruction of brain aneurysms from volumetric image data
- Creators
- David Michael Welch - University of Iowa
- Contributors
- Madhavan L. Raghavan (Advisor)Edwin Dove (Committee Member)David Hasan (Committee Member)Punam Saha (Committee Member)Sarah Vigmostad (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Biomedical Engineering
- Date degree season
- Summer 2010
- Publisher
- University of Iowa
- DOI
- 10.17077/etd.c8d11mr0
- Number of pages
- vii, 91 pages
- Copyright
- Copyright 2010 David Michael Welch
- Language
- English
- Description bibliographic
- Includes bibliographical references (pages 85-91).
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9983777121202771
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