TY - GEN
T1 - Accurate measurement of airway morphology on chest CT images
AU - Nardelli, Pietro
AU - Lanng, Mathias Buus
AU - Møller, Cecilie Brochdorff
AU - Andersen, Anne-Sofie Hendrup
AU - Jørgensen, Alex Skovsbo
AU - Østergaard, Lasse Riis
AU - Estépar, Raúl San José
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-030-00946-5_34
DO - 10.1007/978-3-030-00946-5_34
M3 - Article in proceeding
SN - 978-3-030-00945-8
T3 - Lecture Notes in Computer Science
SP - 335
EP - 347
BT - Image Analysis for Moving Organ, Breast, and Thoracic Images
A2 - Snead, David
A2 - Trucco, Emanuele
A2 - Stoyanov, Danail
A2 - Taylor, Zeike
A2 - Maier-Hein, Lena
A2 - Rajpoot, Nasir
A2 - Bogunovic, Hrvoje
A2 - Ciompi, Francesco
A2 - Veta, Mitko
A2 - Garvin, Mona K.
A2 - Chen, Xin Jan
A2 - Martel, Anne
A2 - van der Laak, Jeroen
A2 - Xu, Yanwu
A2 - McKenna, Stephen
PB - Springer
T2 - 3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018
Y2 - 16 September 2018 through 20 September 2018
ER -