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Title page for ETD etd-03262011-145303


Type of Document Dissertation
Author Ding, Siyi
Author's Email Address siyi.ding@vanderbilt.edu
URN etd-03262011-145303
Title Intraoperative brain shift estimation using vessel segmentation registration and tracking
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Benoit M. Dawant Committee Chair
michael I. Miga Committee Co-Chair
Bobby Bodenheimer Committee Member
J. Michael Fitzpatrick Committee Member
Reid C. Thompson Committee Member
Keywords
  • tracking
  • segmentation
  • registration
  • image guided surgery
  • brain shift
Date of Defense 2010-12-08
Availability unrestricted
Abstract
A typical image-guided neurosurgery system (IGNS) presents images acquired pre-operatively to the surgeons to assist in planning and executing the procedure. The major issue with IGNSs is the fact that the brain shifts during the procedure due to a number of reasons ranging from loss of cerebrospinal fluid, medications, and/or resection of abnormal tissues. Updating the pre-operative images to compensate for brain shift using computational models is an active area of research. In this dissertation, we have developed and evaluated techniques that permit estimating cortical displacements from laser range scanner (LRS) images and intra-operative microscope video sequences. Cortical displacements can be used subsequently as input to computational models. The set of techniques that were developed include novel vessel segmentation algorithms, new registration methods for pre- and post-resection LRS data and new tracking algorithms for microscope video sequences recorded during a tumor resection surgery. Results show that combining LRS images and video sequences is a promising approach to estimate intra-operative brain shift.
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