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Generation of synthetic ground glass nodules using generative adversarial networks (GANs)
BACKGROUND: Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). METHODS: We used 216 computed tomography images with 340 GGNs from the L...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708993/ https://www.ncbi.nlm.nih.gov/pubmed/36447082 http://dx.doi.org/10.1186/s41747-022-00311-y |
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author | Wang, Zhixiang Zhang, Zhen Feng, Ying Hendriks, Lizza E. L. Miclea, Razvan L. Gietema, Hester Schoenmaekers, Janna Dekker, Andre Wee, Leonard Traverso, Alberto |
author_facet | Wang, Zhixiang Zhang, Zhen Feng, Ying Hendriks, Lizza E. L. Miclea, Razvan L. Gietema, Hester Schoenmaekers, Janna Dekker, Andre Wee, Leonard Traverso, Alberto |
author_sort | Wang, Zhixiang |
collection | PubMed |
description | BACKGROUND: Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). METHODS: We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training. RESULTS: Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data. CONCLUSIONS: GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics. |
format | Online Article Text |
id | pubmed-9708993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-97089932022-12-01 Generation of synthetic ground glass nodules using generative adversarial networks (GANs) Wang, Zhixiang Zhang, Zhen Feng, Ying Hendriks, Lizza E. L. Miclea, Razvan L. Gietema, Hester Schoenmaekers, Janna Dekker, Andre Wee, Leonard Traverso, Alberto Eur Radiol Exp Original Article BACKGROUND: Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). METHODS: We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training. RESULTS: Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data. CONCLUSIONS: GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics. Springer Vienna 2022-11-30 /pmc/articles/PMC9708993/ /pubmed/36447082 http://dx.doi.org/10.1186/s41747-022-00311-y Text en © The Author(s) under exclusive licence to European Society of Radiology 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Wang, Zhixiang Zhang, Zhen Feng, Ying Hendriks, Lizza E. L. Miclea, Razvan L. Gietema, Hester Schoenmaekers, Janna Dekker, Andre Wee, Leonard Traverso, Alberto Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title | Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title_full | Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title_fullStr | Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title_full_unstemmed | Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title_short | Generation of synthetic ground glass nodules using generative adversarial networks (GANs) |
title_sort | generation of synthetic ground glass nodules using generative adversarial networks (gans) |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708993/ https://www.ncbi.nlm.nih.gov/pubmed/36447082 http://dx.doi.org/10.1186/s41747-022-00311-y |
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