Abstract
In the last two decades, several methods for airway segmentation from chest CT images have been proposed. The following natural step is the development of a tool to accurately assess the morphology of the bronchial system in all its aspects to help physicians better diagnosis and prognosis complex pulmonary diseases such as COPD, chronic bronchitis and bronchiectasis. Traditional methods for the assessment of airway morphology usually focus on lumen and wall thickness and are often limited due to resolution and artifacts of the CT image. Airway wall cartilage is an important characteristic related to airway integrity that has shown to be deteriorated during the airway disease process. In this paper, we propose the development of a Model-Based GAN Regressor (MBGR) that, thanks to a model-based GAN generator, generate synthetic airway samples with the morphological components necessary to resemble the appearance of real airways on CT at will and that simultaneously measures lumen, wall thickness, and amount of cartilage on pulmonary CT images. The method is evaluated by first computing the relative error on generated images to show that simulating the cartilage helps improve the morphological quantification of the airway structure. We then propose a cartilage index that summarizes the degree of cartilage of bronchial trees structures and perform an indirect validation with subjects with COPD. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways morphology, with the final goal to improve the diagnosis and prognosis of pulmonary diseases.
This work has been partially funded by the National Institutes of Health NHLBI awards R01HL116931, R01HL116473, and R21HL14042. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.
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References
Abadi, M., Agarwal, A., Barham, P., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Chollet, F., et al.: Keras (2015). https://keras.io
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Hogg, J.C., McDonough, J.E., Suzuki, M.: Small airway obstruction in COPD: new insights based on micro-CT imaging and MRI imaging. CHEST 143(5), 1436–1443 (2013)
Kindlmann, G.L., San José Estépar, R., Smith, S.M., Westin, C.F.: Sampling and visualizing creases with scale-space particles. IEEE TVCG 15(6), 1415–1424 (2009)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Nakano, Y., Wong, J.C., de Jong, P.A., et al.: The prediction of small airway dimensions using computed tomography. AJRCCM 171(2), 142–146 (2005)
Nardelli, P., et al.: Accurate measurement of airway morphology on chest CT images. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 335–347. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_34
Nardelli, P., Ross, J.C., Estépar, R.S.J.: CT image enhancement for feature detection and localization. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 224–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_26
Reinhardt, J.M., D’Souza, N., Hoffman, E.A.: Accurate measurement of intrathoracic airways. IEEE TMI 16(6), 820–827 (1997)
Estépar, R.S.J., Washko, G.G., Silverman, E.K., Reilly, J.J., Kikinis, R., Westin, C.-F.: Accurate airway wall estimation using phase congruency. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 125–134. Springer, Heidelberg (2006). https://doi.org/10.1007/11866763_16
Schwab, R.J., Gefter, W.B., Pack, A.I., Hoffman, E.A.: Dynamic imaging of the upper airway during respiration in normal subjects. J. Appl. Physiol. 74(4), 1504–1514 (1993)
Schwarzband, G., Kiryati, N.: The point spread function of spiral CT. Phys. Med. Biol. 50(22), 5307 (2005)
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: IEEE CVPR, vol. 3, p. 6 (2017)
Thurlbeck, W., Pun, R., Toth, J., Frazer, R.: Bronchial cartilage in chronic obstructive lung disease. Am. Rev. Respir. Dis. 109(1), 73–80 (1974)
Weibel, E.R., Cournand, A.F., Richards, D.W.: Morphometry of the Human Lung, vol. 1. Springer, Heidelberg (1963). https://doi.org/10.1007/978-3-642-87553-3
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Nardelli, P., Washko, G.R., San José Estépar, R. (2019). Bronchial Cartilage Assessment with Model-Based GAN Regressor. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_40
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DOI: https://doi.org/10.1007/978-3-030-32226-7_40
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