Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset
For the first time, we have successfully applied computer vision techniques to an OCT dataset of implanted cochleae with fibrosis. Using this deep learning model, the cochlear fibrotic burden calculation can be reliably carried out as we verify in our experimental section.
Authors
- George Burwood (ORCID: https://orcid.org/0000-0001-7779-8389)
- Rubing Xing
- Julia Dietlmeier (ORCID: https://orcid.org/0000-0001-9980-0910)
- Lina A. J. Reiss (ORCID: https://orcid.org/0000-0001-7746-9796)
- Benjamin Greenberg (ORCID: https://orcid.org/0000-0002-7748-5216)
- Jordan Hill (ORCID: https://orcid.org/0000-0001-7560-005X)
- Jingang Yi (ORCID: https://orcid.org/0000-0003-0628-9098)
- Adrienne Fettig
- Teyhana Rounsavill
- Wenxuan He (ORCID: https://orcid.org/0000-0002-2008-3558)
- Noel Edward O'Connor (ORCID: https://orcid.org/0000-0002-4033-9135)
- Teresa Wilson (ORCID: https://orcid.org/0000-0002-2797-6017)
- Madeline Otto
Institutions
- Rutgers, The State University of New Jersey (US)
- Oregon Health & Science University (US)
- Dublin City University (IE)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1109/tbme.2025.3537868
- Citations
- 2
- Primary Topic
- Ear Surgery and Otitis Media
- Type
- article
- Field-Weighted Citation Impact
- 3.70