Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

European Radiology Experimental

Original Article

Lizza E. L. Hendriks¹  Janna Schoenmaekers¹  Andre Dekker²  Alberto Traverso²  Zhixiang Wang²  Leonard Wee²  Zhen Zhang³  Hester Gietema⁴  Razvan L. Miclea⁴  Ying Feng⁵ 

[1] Department of Pulmonary Diseases, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands;Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands;Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands;Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin, China;Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands;Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China;Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands;

关键词: Deep learning;    Tomography (x-ray computed);    Lung;    Neural networks (computer);    Solitary pulmonary nodule;   

DOI  :  10.1186/s41747-022-00311-y

 received in 2022-06-29, accepted in 2022-10-26,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundData 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).MethodsWe 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.ResultsOf 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.ConclusionsGAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better abilityto diagnose whether the data is synthetic than radiomics.

【 授权许可】

CC BY   
© The Author(s) under exclusive licence to European Society of Radiology 2022

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