Generative Adversarial Network for the Segmentation of Ground Glass Opacities and Consolidations from Lung CT Images

Xiaochen Wang, Natalia Khuri

2022

Abstract

The coronavirus disease 2019 is a global pandemic that threatens lives of many people and poses a significant burden for healthcare systems worldwide. Computerized Tomography can detect lung infections, especially in asymptomatic cases, and the detection process can be aided by deep learning. Most of the recent research focused on the segmentation of the entire infected region in a lung. To automate a more fine-grained analysis, a generative adversarial network, comprising two convolutional neural networks, was developed for the segmentation of ground glass opacities and consolidations from tomographic images. The first convolutional neural network acts as a generator of segmented masks, and the second as a discriminator of real and artificially segmented objects, respectively. Experimental results demonstrate that the proposed network outperforms the baseline U-Net segmentation model on the benchmark data set of 929 publicly available images. The dice similarity coefficients of segmenting ground glass opacities and consolidations are 0.664 and 0.625, respectively.

Paper Citation

in Harvard Style

Wang X. and Khuri N. (2022). Generative Adversarial Network for the Segmentation of Ground Glass Opacities and Consolidations from Lung CT Images. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-552-4, SciTePress, pages 27-37. DOI: 10.5220/0010776800003123

in Bibtex Style

@conference{bioinformatics22,
author={Xiaochen Wang and Natalia Khuri},
title={Generative Adversarial Network for the Segmentation of Ground Glass Opacities and Consolidations from Lung CT Images},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS},
year={2022},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010776800003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS
TI - Generative Adversarial Network for the Segmentation of Ground Glass Opacities and Consolidations from Lung CT Images
SN - 978-989-758-552-4
AU - Wang X.
AU - Khuri N.
PY - 2022
SP - 27
EP - 37
DO - 10.5220/0010776800003123
PB - SciTePress