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).
Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT
imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for
lung texture. This paper presents an original image pre-processing framework based on locally connected filtering
applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung
texture classification. By removing the dense vascular network from images used by the CNN for lung classification,
locally connected filters provide a better discrimination between different lung patterns and help regularizing the
classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung
pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the
art cascade of CNNs for this task.
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