# Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

## TL;DR

This paper introduces a Bayesian changepoint detection method to accurately identify airway dilatation progression in lung images, demonstrating robustness against noise and variability, with potential for disease monitoring.

## Contribution

A novel probabilistic model and inference approach for detecting airway dilatation changes from longitudinal imaging data, improving accuracy and robustness over existing methods.

## Key findings

- Detected airway dilatation with 2.5mm accuracy on simulated data
- Reasonable agreement with radiologists on real IPF data
- Quantified airway volume change for disease progression assessment

## Abstract

Numerous lung diseases, such as idiopathic pulmonary fibrosis (IPF), exhibit dilation of the airways. Accurate measurement of dilatation enables assessment of the progression of disease. Unfortunately the combination of image noise and airway bifurcations causes high variability in the profiles of cross-sectional areas, rendering the identification of affected regions very difficult. Here we introduce a noise-robust method for automatically detecting the location of progressive airway dilatation given two profiles of the same airway acquired at different time points. We propose a probabilistic model of abrupt relative variations between profiles and perform inference via Reversible Jump Markov Chain Monte Carlo sampling. We demonstrate the efficacy of the proposed method on two datasets; (i) images of healthy airways with simulated dilatation; (ii) pairs of real images of IPF-affected airways acquired at 1 year intervals. Our model is able to detect the starting location of airway dilatation with an accuracy of 2.5mm on simulated data. The experiments on the IPF dataset display reasonable agreement with radiologists. We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression. The code is available at https://github.com/quan14/Modelling_Airway_Geometry_as_Stock_Market_Data