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 NeuralOCT, a learning-based approach to process airway OCT images. Specifically, 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 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.
@article{arxiv.2403.10622,
title = {NeuralOCT: Airway OCT Analysis via Neural Fields},
author = {Yining Jiao and Amy Oldenburg and Yinghan Xu and Srikamal Soundararajan and Carlton Zdanski and Julia Kimbell and Marc Niethammer},
journal= {arXiv preprint arXiv:2403.10622},
year = {2024}
}