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Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection

Quan, K; Tanno, R; Duong, M; Nair, A; Shipley, R; Jones, M; Brereton, C; ... Jacob, J; + view all (2019) Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection. In: Suk, H and Liu, M and Yan, P and Lian, C, (eds.) MLMI 2019: Machine Learning in Medical Imaging. (pp. pp. 345-354). Springer: Shenzhen, China. Green open access

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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 2.5 mm 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.

Type: Proceedings paper
Title: Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection
Event: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32692-0_40
Publisher version: https://doi.org/10.1007/978-3-030-32692-0_40
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10087906
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