Towards automatic identification of middle ear structures in endoscopic OCT

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Abstract

Endoscopic optical coherence tomography (OCT) enables the assessment of the eardrum and the middle ear in vivo. However, revealing the ossicles is often limited due to shadowing effects of preceding structures and the 3D impression is difficult to interpret. To compare the identified middle ear structures, OCT and cone-beam CT of a patient were spatially aligned and showed a good agreement in locating malleus and the promontory wall. As CT imaging uses ionizing radiation and is thus limited in application, we furthermore provide a concept how radiology can be utilized as a priori knowledge for OCT imaging. Therefore, a statistical shape model derived from µCT data of temporal bone specimens was fitted to in vivo OCT measurements, potentially providing a real-time augmentation of endoscopic OCT for middle ear diagnostics in the future.

Details

Original languageEnglish
Title of host publicationOptical Coherence Imaging Techniques and Imaging in Scattering Media V
EditorsBenjamin J. Vakoc, Maciej Wojtkowski, Yoshiaki Yasuno
PublisherSPIE - The international society for optics and photonics
ISBN (electronic)9781510664739
Publication statusPublished - 2023
Peer-reviewedYes

Publication series

SeriesProceedings of SPIE - The International Society for Optical Engineering
Volume12632
ISSN0277-786X

Conference

TitleOptical Coherence Imaging Techniques and Imaging in Scattering Media V
Conference number5
Duration25 - 29 June 2023
Degree of recognitionInternational event
CityMünchen
CountryGermany

External IDs

ORCID /0009-0002-1419-8698/work/145224172
ORCID /0000-0002-8160-3000/work/145224307
ORCID /0000-0003-0554-2178/work/145224486
ORCID /0009-0008-7642-8608/work/145224875
ORCID /0000-0002-4590-1908/work/163293971

Keywords

Keywords

  • micro-computed tomography, middle ear, optical coherence tomography, ossicles, statistical shape model