Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection

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.

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

“…Using synthetic data, the papers were able to mimic real IPF scenarios. Quan et al 43 also validated their results with labels provided by radiologists on several examples. In Pakzad A et al, 42 the synthesized airways were later used for Cox regression analysis.…”

“…Quan et al 43 also focus on the prediction of airway dilation, but in this case, also over time. They presented a probabilistic model to identify the regions of progressive airway dilation, given two profiles acquired at different time points.…”