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You searched for +publisher:"Vanderbilt University" +contributor:("Dr. Benoit M. Dawant"). Showing records 1 – 3 of 3 total matches.

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Vanderbilt University

1. Reda, Fitsum Aklilu. Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming.

Degree: PhD, Electrical Engineering, 2014, Vanderbilt University

A cochlear implant (CI) is a neural prosthetic device that restores hearing by directly stimulating the auditory nerve using an electrode array surgically placed in the cochlea. Conventional CI implantation techniques require major excavation of the skull to achieve access and place an electrode array into the cochlea. After placement, CIs are typically programmed to attempt to optimize hearing outcome. Recently, image-guidance has been proposed to minimize the invasiveness of conventional CI surgery techniques as well as to design new strategies to improve CI programming outcomes. These image-guided techniques necessitate the automatic segmentation of the structures of the ear in pre- or post-implantation CTs, or the automatic registration of pre- and intra-implantation CTs. The structures of the ear include the facial nerve, the chorda tympani, the labyrinth, the ear canal, the tympani membrane, the ossicles, and the inner ear structures, which include the scala tympani, the scala vestibuli and the spiral ganglion. In this dissertation, we present a set of innovative image processing techniques we have developed to achieve the necessary segmentation or registration tasks. The set of techniques includes methods for automatic segmentation of the structures of the ear in pediatric pre-implantation CT, a new pose-invariant pre- to intra-implantation CT registration method, new algorithms for automatic segmentation of the inner ear structures in post-unilateral-implantation CT, and novel shape library-based algorithms for automatic segmentation of the inner ear structures in post-bilateral-implantation CT. All these techniques have been validated both qualitatively, by experts in ear anatomy, and quantitatively, by comparing the results they produce to expert generated results. Advisors/Committee Members: Dr. J. Michael Fitzpatrick (committee member), Dr. Robert F. Labadie (committee member), Dr. Jack H. Noble (committee member), Dr. Robert J. Webster III (committee member), Dr. Benoit M. Dawant (Committee Chair).

Subjects/Keywords: Image Segmentation; Image Registration; Statistical Shape Models; Surface-to-Image Registration; Shape Alignment; Cochlear Imaplnt; Cochlear Implant Surgery; Cochlear Implant Programming; CT; Ear; Minimally-invasive Surgery

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Reda, F. A. (2014). Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11124

Chicago Manual of Style (16th Edition):

Reda, Fitsum Aklilu. “Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/11124.

MLA Handbook (7th Edition):

Reda, Fitsum Aklilu. “Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming.” 2014. Web. 18 Jan 2021.

Vancouver:

Reda FA. Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/11124.

Council of Science Editors:

Reda FA. Automatic Segmentation of Structures and Registration of CT Images for Image-Guided Otologic Surgery and Implant Programming. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/11124


Vanderbilt University

2. Joshi, Pallavi Vilas. Automatic segmentation of brain structures for radiotherapy planning.

Degree: MS, Electrical Engineering, 2005, Vanderbilt University

In the past few decades unprecedented advances have been made in the field of medical imaging. Various imaging technologies such as Computed Tomography, Magnetic Resonance Imaging, etc. have emerged to assist the visualization of internal structures in the body. These along with the different image processing tools help in diagnosis and detection of diseases. Intensity Modulated Radiation Therapy (IMRT) is a recently developed and highly effective method for destroying cancerous cells with minimal effect on the other body structures of the patient. It relies on accurate delineation of the structures to be irradiated and those to be spared. Currently the delineation is being done manually, which is very time consuming. We have therefore proposed an atlas-based automatic segmentation method which will significantly reduce the interaction time during radiotherapy planning. In this thesis, the main focus is on improving the results obtained by the atlas-based segmentation method. Three methods have been implemented namely CT-MR fusion method, Mesh deformations and Classifier combination method. We have employed two methods for validating the automatically generated results. This is done by comparing the automatic masks and contours with the manual ground truth segmentation. Finally, the different methods have been compared and the feasibility of automatic delineation has been discussed. Advisors/Committee Members: Dr. Mike Fitzpatrick (committee member), Dr. Benoit M. Dawant (committee member).

Subjects/Keywords: Registration; atlas-based segmentation; Classifier combination; Validation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Joshi, P. V. (2005). Automatic segmentation of brain structures for radiotherapy planning. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11802

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Joshi, Pallavi Vilas. “Automatic segmentation of brain structures for radiotherapy planning.” 2005. Thesis, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/11802.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Joshi, Pallavi Vilas. “Automatic segmentation of brain structures for radiotherapy planning.” 2005. Web. 18 Jan 2021.

Vancouver:

Joshi PV. Automatic segmentation of brain structures for radiotherapy planning. [Internet] [Thesis]. Vanderbilt University; 2005. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/11802.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Joshi PV. Automatic segmentation of brain structures for radiotherapy planning. [Thesis]. Vanderbilt University; 2005. Available from: http://hdl.handle.net/1803/11802

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Vanderbilt University

3. Pallavaram Srinivasan, Srivatsan. Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift.

Degree: PhD, Electrical Engineering, 2010, Vanderbilt University

Chronic Deep Brain Stimulation (DBS) has been a rapidly evolving area of neurotherapeutics since its initial introduction for the treatment of Parkinson’s disease and essential tremor in the 1990s. In the recent past, there has been active research to improve the outcome of the procedure as well as to make it more accessible to patients. This dissertation is broadly categorized into two parts. The first is motivated by a lack of standardization in the localization of popular anatomical landmarks used to indirectly localize as well as communicate stereotactic targets. Inter-surgeon variability in manually selecting these landmarks and its impact on target localization is shown to be substantial. A method based on non-rigid image registration is used for automatic prediction of the landmarks and its accuracy is shown to be sub-millimetric in both clinical and non-clinical settings. The second part is motivated by shortcomings and inaccuracies in existing methods to populate statistical atlases of electrophysiological data acquired intra-operatively during DBS surgeries. A Gaussian smoothed spherical shell kernel is proposed as an improvement over an existing method to model stimulation response in order to build accurate statistical maps. The effect of intra-operative brain shift on the creation of electrophysiological atlases is investigated and shown to be substantial. An approach to build low-shift atlases is proposed and statistical maps of stimulation response built using data from such an atlas are shown to correlate strongly with a statistical ground truth as well as with an anatomical atlas. Finally, in a preliminary study, it is shown that statistical maps of adverse effects combined with statistical maps of efficacious stimulation response could be clinically useful for post-operative programming assistance in DBS. Advisors/Committee Members: Dr. J. Michael Fitzpatrick (committee member), Dr. Joseph S. Neimat (committee member), Dr. Peter E. Konrad (committee member), Dr. Robert Bodenheimer (committee member), Dr. Benoit M. Dawant (Committee Chair).

Subjects/Keywords: non-rigid registration; statistical maps; pre-operative planning; intra-operative navigation or guidance; post-operative programming; electrophysiological atlases; Deep Brain Stimulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pallavaram Srinivasan, S. (2010). Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13019

Chicago Manual of Style (16th Edition):

Pallavaram Srinivasan, Srivatsan. “Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift.” 2010. Doctoral Dissertation, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/13019.

MLA Handbook (7th Edition):

Pallavaram Srinivasan, Srivatsan. “Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift.” 2010. Web. 18 Jan 2021.

Vancouver:

Pallavaram Srinivasan S. Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift. [Internet] [Doctoral dissertation]. Vanderbilt University; 2010. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/13019.

Council of Science Editors:

Pallavaram Srinivasan S. Standardizing indirect targeting and building electrophysiological maps for deep brain stimulation surgery after accounting for brain shift. [Doctoral Dissertation]. Vanderbilt University; 2010. Available from: http://hdl.handle.net/1803/13019

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