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dc.contributor.advisor Goldman, Ron
dc.creatorYuan, Binhang
dc.date.accessioned 2017-08-02T17:52:01Z
dc.date.available 2017-08-02T17:52:01Z
dc.date.created 2016-05
dc.date.issued 2016-02-11
dc.date.submitted May 2016
dc.identifier.citation Yuan, Binhang. "A computational framework for evaluating outcomes in infant craniosynostosis reconstruction." (2016) Master’s Thesis, Rice University. https://hdl.handle.net/1911/96228.
dc.identifier.urihttps://hdl.handle.net/1911/96228
dc.description.abstract Historically, surgical outcomes in craniosynostosis have been evaluated by qualitative analysis, direct and indirect anthropometry, cephalometrics, and CT craniometric analysis. Three-dimensional meshes constructed from 3dMD images acquired on patients with synostosis at multiple times across the course of surgical treatment provide ideal raw data for a novel approach to 3D geometric shape analysis of surgical results. We design a automatic computational framework for evaluating and visualizing the results of infant cranial surgeries based on 3dMD images. The goal of this framework is to assist surgeons in evaluating the efficacy of their surgical techniques. Feedback from surgeons in Texas Children's Hospital confirms that this framework is a robust computational system within which surgical outcomes in synostosis can be accurately and meaningfully evaluated. We also propose an algorithm to generate normative infant cranial models from the input of 3D meshes, which are extracted from CT scans of normal infant skulls. Comparing of the head shape of an affected subject with a normal control will more clearly illustrate in what aspect the subject's head deviates from the norm. Comparing of a post-treatment subject's head shape and an age-matched control would allow assessing of a specific treatment approach or surgical technique.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectSurgery Evaluation
Visualization
Curvature
Normal Vecter
dc.title A computational framework for evaluating outcomes in infant craniosynostosis reconstruction
dc.type Thesis
dc.date.updated 2017-08-02T17:52:01Z
dc.type.material Text
thesis.degree.department Computer Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science


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