Nishio Mizuho, Fujimoto Koji, Matsuo Hidetoshi, Muramatsu Chisako, Sakamoto Ryo, Fujita Hiroshi
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Department of Radiology, Kobe University Hospital, Kobe, Japan.
Front Artif Intell. 2021 Jul 16;4:694815. doi: 10.3389/frai.2021.694815. eCollection 2021.
The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial network (GAN). Three public datasets containing images of lung nodules/lung cancers were used: LUNA16 dataset, Decathlon lung dataset, and NSCLC radiogenomics. The LUNA16 dataset was used to generate an artificial dataset for lung cancer segmentation with the help of the GAN and 3D graph cut. Pretrained models were then constructed from the artificial dataset. Subsequently, the main segmentation model was constructed from the pretrained models and the Decathlon lung dataset. Finally, the NSCLC radiogenomics dataset was used to evaluate the main segmentation model. The Dice similarity coefficient (DSC) was used as a metric to evaluate the segmentation performance. The mean DSC for the NSCLC radiogenomics dataset improved overall when using the pretrained models. At maximum, the mean DSC was 0.09 higher with the pretrained model than that without it. The proposed method comprising an artificial dataset and a pretrained model can improve lung cancer segmentation as confirmed in terms of the DSC metric. Moreover, the construction of the artificial dataset for the segmentation using the GAN and 3D graph cut was found to be feasible.
本研究的目的是利用预训练模型和迁移学习来开发和评估肺癌分割。预训练模型由使用生成对抗网络（GAN）生成的人工数据集构建而成。使用了三个包含肺结节/肺癌图像的公共数据集：LUNA16数据集、十项全能肺数据集和NSCLC放射基因组学数据集。LUNA16数据集借助GAN和3D图割用于生成肺癌分割的人工数据集。然后从人工数据集构建预训练模型。随后，从预训练模型和十项全能肺数据集构建主要分割模型。最后，使用NSCLC放射基因组学数据集评估主要分割模型。使用骰子相似系数（DSC）作为评估分割性能的指标。使用预训练模型时，NSCLC放射基因组学数据集的平均DSC总体上有所提高。使用预训练模型时，平均DSC最高比不使用时高0.09。如DSC指标所证实，所提出的包含人工数据集和预训练模型的方法可以改善肺癌分割。此外，发现使用GAN和3D图割构建用于分割的人工数据集是可行的。
