NeuralOCT: Airway OCT Analysis via Neural Fields
Yining Jiao Amy Oldenburg Yinghan Xu Srikamal Soundararajan Carlton Zdanski Julia Kimbell Marc Niethammer
Optical coherence tomography (OCT) is a popular modality in ophthalmology and
is also used intravascularly. Our interest in this work is OCT in the context
of airway abnormalities in infants and children where the high resolution of
OCT and the fact that it is radiation-free is important. The goal of airway OCT
is to provide accurate estimates of airway geometry (in 2D and 3D) to assess
airway abnormalities such as subglottic stenosis. We propose
$\texttt{NeuralOCT}$, a learning-based approach to process airway OCT images.
Specifically, $\texttt{NeuralOCT}$ extracts 3D geometries from OCT scans by
robustly bridging two steps: point cloud extraction via 2D segmentation and 3D
reconstruction from point clouds via neural fields. Our experiments show that
$\texttt{NeuralOCT}$ produces accurate and robust 3D airway reconstructions
with an average A-line error smaller than 70 micrometer. Our code will cbe
available on GitHub.