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.

我们提出了一种名为 NeuralOCT 的学习方法，用于处理气道 OCT 图像，从而提取 2D 图像中的 3D 几何形状，并通过神经场从点云中进行 3D 重构，实验证明 NeuralOCT 可以产生准确且稳定的 3D 气道重建结果，平均 A 线误差小于 70 微米。