tetano
Editor, Senior Moderator
Biomed Signal Process Control
. 2021 Apr 1;102582.
doi: 10.1016/j.bspc.2021.102582. Online ahead of print.
Evaluation of COVID-19 chest computed tomography: a texture analysis based on three-dimensional entropy
Andreia S Gaud?ncio[SUP] 1 [/SUP], Pedro G Vaz[SUP] 1 [/SUP], Mirvana Hilal[SUP] 2 [/SUP], Guillaume Mah?[SUP] 3 [/SUP], Mathieu Lederlin[SUP] 3 [/SUP], Anne Humeau-Heurtier[SUP] 2 [/SUP], Jo?o M Cardoso[SUP] 1 [/SUP]
Affiliations
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.
. 2021 Apr 1;102582.
doi: 10.1016/j.bspc.2021.102582. Online ahead of print.
Evaluation of COVID-19 chest computed tomography: a texture analysis based on three-dimensional entropy
Andreia S Gaud?ncio[SUP] 1 [/SUP], Pedro G Vaz[SUP] 1 [/SUP], Mirvana Hilal[SUP] 2 [/SUP], Guillaume Mah?[SUP] 3 [/SUP], Mathieu Lederlin[SUP] 3 [/SUP], Anne Humeau-Heurtier[SUP] 2 [/SUP], Jo?o M Cardoso[SUP] 1 [/SUP]
Affiliations
- PMID: 33824680
- PMCID: PMC8015668
- DOI: 10.1016/j.bspc.2021.102582
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.