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.
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Notes
- 1.
https://www.mathworks.com/help/signal/ref/findchangepts.html last accessed on August 16, 2019.
References
Chib, S., et al.: Understanding the metropolis-hastings algorithm. Am. Stat. 49(4), 327–335 (1995)
Gazourian, L., et al.: Quantitative computed tomography assessment of bronchiolitis obliterans syndrome after lung transplantation. Clin. Transplant. 31(5), e12943 (2017)
Gelman, A., et al.: Bayesian Data Analysis. CRC Press, Boca Raton (2014)
Green, P.J.: Reversible jump Markov chain monte carlo computation and Bayesian model determination. Biometrika 82(4), 711–732 (1995)
Green, P.J., et al.: Reversible jump MCMC. Genetics 155(3), 1391–1403 (2009)
Gu, S., et al.: Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach. Med. Image Anal. 17(3), 283–96 (2013)
Jacob, J., et al.: HRCT of fibrosing lung disease. Respirology 20(6), 859–872 (2015)
Jacob, J., et al.: Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores. Eur. Radiol. 28(3), 1318–1327 (2018)
Konietzke, P., et al.: Quantitative CT detects changes in airway dimensions and air-trapping after bronchial thermoplasty for severe asthma. Eur. J. Radiol. 107, 33–38 (2018)
Lavielle, M.: Using penalized contrasts for the change-point problem. Signal Process. 85(8), 1501–1510 (2005)
Lederer, D., et al.: Idiopathic pulmonary fibrosis. New Engl. J. Med. 378(19), 1811–1823 (2018)
Mikosch, T., et al.: Changes of structure in financial time series and the GARCH model. REVSTAT Stat. J. 2(1), 41–73 (2004)
Palágyi, K., et al.: Quantitative analysis of pulmonary airway tree structures. Comput. Biol. Med. 36(9), 974–96 (2006)
Prince, S.J.D.: Computer Vision: Models, Learning, and Inference. Cambridge University Press, Cambridge (2012)
Quan, K., et al.: Tapering analysis of airways with bronchiectasis. In: Proceedings of SPIE (2018)
Weibel, E.R.: Morphometry of the Human Lung. Springer-Verlag, Heidelberg (1963). https://doi.org/10.1007/978-3-642-87553-3
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Quan, K. et al. (2019). Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_40
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DOI: https://doi.org/10.1007/978-3-030-32692-0_40
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