Deep-learning characterization and quantification of COVID-19 pneumonia lesions from chest CT images

Bermejo-Pelaez, D., San José Estépar, R., Fernández-Velilla, M., Palacios Miras, C., Gallardo Madueño, G., & Benegas, M. et al. (2022). Deep-learning characterization and quantification of COVID-19 pneumonia lesions from chest CT images. Medical Imaging 2022: Computer-Aided Diagnosis. doi: 10.1117/12.2613086
ABSTRACT

A relevant percentage of COVID-19 patients present bilateral pneumonia. Disease progression and healing is characterized by the presence of different parenchymal lesion patterns. Artificial intelligence algorithms have been developed to identify and assess the related lesions and properly segment affected lungs, however very little attention has been paid to automatic lesion subtyping. In this work we present artificial intelligence algorithms based on CNN to automatically identify and quantify COVID-19 pneumonia patterns. A Dense-efficient CNN architecture is presented to
automatically segment the different lesion subtypes. The proposed technique has been independently tested in a multicentric cohort of 100 patients, showing Dice coefficients of 0.988±0.01 for ground glass opacities, 0.948±0.05 for consolidations, and 0.999±0.0003 for healthy tissue with respect to radiologist’s reference segmentations, and high correlations with respect to radiologist severity visual scores.

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