An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease using HRCT Images from Different Vendors

This study proposes the use of ensemble classifiers to address inter-scanner variations in differentiating regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients from different scanners. The experiment involved 600 regions of interest (ROIs) and 92 HRCT images from two different scanners, classified by expert radiologists. Textual and shape features were extracted from the ROIs and the whole lung parenchyma. Ensemble classifiers showed higher accuracy compared to individual classifiers in the integrated scanner test, as well as partial improvements in intra- and inter-scanner tests. In the whole lung classification, the ensemble classifiers achieved higher quantification accuracies compared to individual classifiers. Overall, the study concludes that ensemble classifiers perform better, particularly with integrated scanner images, providing a promising approach to address inter-scanner variations in HRCT analysis. The algorithm has been comercialized in AVIEW Lung Texture, Coreline Soft.