# Endoscopic navigation in the absence of CT imaging

arXiv: https://arxiv.org/abs/1806.03997 · HTML: https://mlsanity.com/paper/1806.03997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation](https://mlsanity.com/paper/2109.11798/) (2021)
- [Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images](https://mlsanity.com/paper/2504.17582/) (2025)

## Abstract (arXiv)

Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician. In this paper, we present a system for navigation during clinical endoscopic exploration in the absence of computed tomography (CT) scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm along with dense reconstructions from video, we show that we are able to achieve submillimeter registrations in in-vivo clinical data and are able to assign confidence to these registrations using confidence criteria established using simulated data.