Publication

Lung topology characteristics in patients with chronic obstructive pulmonary disease

Journal Article (2018)

Journal

Scientific Reports

Pages

5341

Volume

8

Number

1

Doc link

https://doi.org/10.1038/s41598-018-23424-0

File

Download the digital copy of the doc pdf document

Authors

Abstract

Quantitative features that can currently be obtained from medical imaging do not provide a complete picture of Chronic Obstructive Pulmonary Disease (COPD). In this paper, we introduce a novel analytical tool based on persistent homology that extracts quantitative features from chest CT scans to describe the geometric structure of the airways inside the lungs. We show that these new radiomic features stratify COPD patients in agreement with the GOLD guidelines for COPD and can distinguish between inspiratory and expiratory scans. These CT measurements are very different to those currently in use and we demonstrate that they convey significant medical information. The results of this study are a proof of concept that topological methods can enhance the standard methodology to create a finer classification of COPD and increase the possibilities of more personalized treatment.

Categories

computer vision, feature extraction, image classification, pattern clustering.

Author keywords

Chronic obstructive pulmonary disease, Topological data analysis, Radiomic features

Scientific reference

F. Belchí, M. Pirashvili, J. Conway, M. Bennett, R. Djukanovic and J. Brodzki. Lung topology characteristics in patients with chronic obstructive pulmonary disease. Scientific Reports, 8(1): 5341, 2018.