The Dresden in vivo OCT dataset for automatic middle ear segmentation

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

Endoscopic optical coherence tomography (OCT) offers a non-invasive approach to perform the morphological and functional assessment of the middle ear in vivo. However, interpreting such OCT images is challenging and time-consuming due to the shadowing of preceding structures. Deep neural networks have emerged as a promising tool to enhance this process in multiple aspects, including segmentation, classification, and registration. Nevertheless, the scarcity of annotated datasets of OCT middle ear images poses a significant hurdle to the performance of neural networks. We introduce the Dresden in vivo OCT Dataset of the Middle Ear (DIOME) featuring 43 OCT volumes from both healthy and pathological middle ears of 29 subjects. DIOME provides semantic segmentations of five crucial anatomical structures (tympanic membrane, malleus, incus, stapes and promontory), and sparse landmarks delineating the salient features of the structures. The availability of these data facilitates the training and evaluation of algorithms regarding various analysis tasks with middle ear OCT images, e.g. diagnostics.

Details

OriginalspracheEnglisch
Aufsatznummer242
Seitenumfang7
FachzeitschriftScientific data
Jahrgang11 (2024)
Ausgabenummer1
PublikationsstatusVeröffentlicht - 26 Feb. 2024
Peer-Review-StatusJa

Externe IDs

ORCID /0009-0002-1419-8698/work/154741460
ORCID /0000-0002-8160-3000/work/154741696
ORCID /0000-0003-0554-2178/work/154741779
ORCID /0009-0008-7642-8608/work/154742281
unpaywall 10.1038/s41597-024-03000-0
Scopus 85186407493
ORCID /0000-0002-4590-1908/work/163294012

Schlagworte

Schlagwörter

  • Humans, Tomography, Optical Coherence/methods, Ear, Middle/diagnostic imaging, Neural Networks, Computer, Algorithms