Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs.

ABSTRACT:

Purpose

To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically.

Materials and methods

This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia

SUBMITTER: Yoo SJ 

PROVIDER: S-EPMC10181872 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature