Accurate measurement of airway morphology on chest CT images

Pietro Nardelli, Mathias Buus Lanng, Cecilie Brochdorff Møller, Anne-Sofie Hendrup Andersen, Alex Skovsbo Jørgensen, Lasse Riis Østergaard, Raúl San José Estépar

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

3 Citationer (Scopus)

Abstrakt

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.

OriginalsprogEngelsk
TitelImage Analysis for Moving Organ, Breast, and Thoracic Images : Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings
RedaktørerDavid Snead, Emanuele Trucco, Danail Stoyanov, Zeike Taylor, Lena Maier-Hein, Nasir Rajpoot, Hrvoje Bogunovic, Francesco Ciompi, Mitko Veta, Mona K. Garvin, Xin Jan Chen, Anne Martel, Jeroen van der Laak, Yanwu Xu, Stephen McKenna
Antal sider13
ForlagSpringer
Publikationsdato2018
Sider335-347
ISBN (Trykt)978-3-030-00945-8
ISBN (Elektronisk)978-3-030-00946-5
DOI
StatusUdgivet - 2018
Begivenhed3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018 - Granada, Spanien
Varighed: 16 sep. 201820 sep. 2018

Konference

Konference3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018
LandSpanien
ByGranada
Periode16/09/201820/09/2018
NavnLecture Notes in Computer Science
Vol/bind11040
ISSN0302-9743

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Citationsformater

Nardelli, P., Lanng, M. B., Møller, C. B., Andersen, A-S. H., Jørgensen, A. S., Østergaard, L. R., & Estépar, R. S. J. (2018). Accurate measurement of airway morphology on chest CT images. I D. Snead, E. Trucco, D. Stoyanov, Z. Taylor, L. Maier-Hein, N. Rajpoot, H. Bogunovic, F. Ciompi, M. Veta, M. K. Garvin, X. J. Chen, A. Martel, J. van der Laak, Y. Xu, & S. McKenna (red.), Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings (s. 335-347). Springer. Lecture Notes in Computer Science, Bind. 11040 https://doi.org/10.1007/978-3-030-00946-5_34