TY - GEN
T1 - Image biomarkers for quantitative analysis of idiopathic interstitial pneumonia
AU - Kim, Young Wouk
AU - Tarando, Sebastián Roberto
AU - Brillet, Pierre Yves
AU - Fetita, Catalin
N1 - Publisher Copyright:
© 2019 SPIE.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - As a subclass of interstitial lung diseases, fibrosing idiopathic interstitial pneumonia (IIP), whose cause is mostly unknown, is a continuous and irreversible process, manifesting as progressive worsening of lung function. Quantifying the evolution of the patient status imposes the development of automated CAD tools to depict the pathology occurrence in the lung but also an associated severity degree. In this paper we propose several biomarkers for IIP quantification, associating spatial localization of the disease using lung texture classification, and severity measures in relation with vascular and bronchial remodeling which correlate with clinical parameters. We follow-up our work on lung texture analysis based on convolutional neural networks (reporting an increased performance in sensitivity, specificity and accuracy) on an enlarged training/testing database (110/20 patients respectively). The area under the curve (AUC:2-6) for vessel calibers distribution between 2-6 mm radii (evaluated in 70 patients) showed up as a promising biomarker of the severity of the disease, independently of the extent of lesions, correlating with the composite physiologic index. In the same way, normalized airway lobe length, normalized airway lobe volume and the score of distal airway caliber deviation from the physiologically power decrease law correlated with radiologic severity score, manifesting as potential biomarkers of traction bronchiectasis (assessment in 18 patients).
AB - As a subclass of interstitial lung diseases, fibrosing idiopathic interstitial pneumonia (IIP), whose cause is mostly unknown, is a continuous and irreversible process, manifesting as progressive worsening of lung function. Quantifying the evolution of the patient status imposes the development of automated CAD tools to depict the pathology occurrence in the lung but also an associated severity degree. In this paper we propose several biomarkers for IIP quantification, associating spatial localization of the disease using lung texture classification, and severity measures in relation with vascular and bronchial remodeling which correlate with clinical parameters. We follow-up our work on lung texture analysis based on convolutional neural networks (reporting an increased performance in sensitivity, specificity and accuracy) on an enlarged training/testing database (110/20 patients respectively). The area under the curve (AUC:2-6) for vessel calibers distribution between 2-6 mm radii (evaluated in 70 patients) showed up as a promising biomarker of the severity of the disease, independently of the extent of lesions, correlating with the composite physiologic index. In the same way, normalized airway lobe length, normalized airway lobe volume and the score of distal airway caliber deviation from the physiologically power decrease law correlated with radiologic severity score, manifesting as potential biomarkers of traction bronchiectasis (assessment in 18 patients).
KW - Convolutional networks
KW - Deep learning
KW - Fibrosing idiopathic interstitial pneumonia
KW - Image biomarker
KW - Infiltrative lung diseases
KW - Locally connected filters
KW - Lung texture classification
KW - Mathematical morphology
KW - Traction bronchiectasis
KW - Vascular remodeling
UR - https://www.scopus.com/pages/publications/85068187275
U2 - 10.1117/12.2511847
DO - 10.1117/12.2511847
M3 - Conference contribution
AN - SCOPUS:85068187275
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Mori, Kensaku
A2 - Hahn, Horst K.
PB - SPIE
T2 - Medical Imaging 2019: Computer-Aided Diagnosis
Y2 - 17 February 2019 through 20 February 2019
ER -