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. The code is available at https://github.com/quan14/Modelling_Airway_Geometry_as_Stock_Market_Data
Cite
@article{arxiv.1906.12225,
title = {Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection},
author = {Kin Quan and Ryutaro Tanno and Michael Duong and Arjun Nair and Rebecca Shipley and Mark Jones and Christopher Brereton and John Hurst and David Hawkes and Joseph Jacob},
journal= {arXiv preprint arXiv:1906.12225},
year = {2019}
}
Comments
14 pages, 7 figures, Accepted to The 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019). In conjunction with MICCAI 2019, Shenzhen, China