Deep-Learning-Enhanced Atlas-Based Preoperative and Intraoperative Registration for Cochlear Implant Surgery Navigation

Simple item page
dc.contributor.advisorNoble, Jack
dc.contributor.committeeChairNoble, Jack
dc.creatorZhang, Yike
dc.creator.orcid0000-0003-3503-2996
dc.date.accessioned2025-09-26T11:11:06Z
dc.date.available2025-09-26T11:11:06Z
dc.date.created2025-08
dc.date.issued2025-05-22
dc.date.submittedAugust 2025
dc.description.abstractThis dissertation provides the groundwork for intraoperative registration in cochlear implant surgery though the developed Vision6D pose annotation tool and a series of deep-learning-based methods. The primary contributions can be summarized as follows. First, self-supervised ossicles registration and segmentation, as detailed in Chapter 2. Second, the development of the Vision6D software and its comprehensive user study using two public 6D pose estimation datasets, as introduced in Chapter 3. Third, 2D monocular microscope views to 3D CT registration using the incus of the ossicles as a landmark, which is described in Chapter 4. Fourth, mastoidectomy shape prediction to extract the postmastoidectomy mesh directly from preoperative CT scans, as shown in Chapters 5, 6, and 7. Fifth, postmastoidectomy surface multi-view synthesis from a single microscope image is proposed in Chapter 8. Sixth, surgical scene completion for the synthetic postmastoidectomy surface multi-views through single-step denoising diffusion GAN, as illustrated in Chapter 9. Finally, Chapter 10 utilizes the prior contributions from Chapters 5 to 8 to perform the monocular patient-to-image intraoperative registration for cochlear implant surgery that leverages the synthetic surgical views. These combined components provide numerous opportunities for future intraoperative navigation systems and surgical applications.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/1803/19891
dc.language.isoen
dc.subjectCochlear Implant, Deep Learning, Intraoperative Registration, Atlas-based Registration and Segmentation.
dc.titleDeep-Learning-Enhanced Atlas-Based Preoperative and Intraoperative Registration for Cochlear Implant Surgery Navigation
dc.typeThesis
dc.type.materialtext
thesis.degree.disciplineComputer Science
thesis.degree.grantorVanderbilt University Graduate School
thesis.degree.levelDoctoral
thesis.degree.namePhD
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ZHANG-DISSERTATION-2025.pdf
Size:
31.08 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.92 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.25 KB
Format:
Plain Text
Description:
Download
Collections
Electronic Theses and Dissertations