Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy

Andreia S Gaudêncio; Pedro G Vaz; Mirvana Hilal; Guillaume Mahé; Mathieu Lederlin; Anne Humeau-Heurtier; João M Cardoso

doi:10.1016/j.bspc.2021.102582

Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy

Biomed Signal Process Control. 2021 Jul:68:102582. doi: 10.1016/j.bspc.2021.102582. Epub 2021 Apr 1.

Authors

Andreia S Gaudêncio¹, Pedro G Vaz¹, Mirvana Hilal², Guillaume Mahé³, Mathieu Lederlin³, Anne Humeau-Heurtier², João M Cardoso¹

Affiliations

¹ LIBPhys-UC, Department of Physics, University of Coimbra, Coimbra, 3004-516, Portugal.
² Univ Angers, LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes, 62 Avenue Notre-Dame du Lac, Angers, 49000, France.
³ UniversityHospital of Rennes, Rennes, 35000, France.

Abstract

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( $p < 0.01$ ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of $89.6 %$ , a sensitivity of $96.1 %$ , and a specificity of $76.9 %$ . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

Keywords: COVID-19; Computed tomography; Multiscale entropy; Texture analysis.