Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to provide a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where contrast is especially poor owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the interslice connection data that the existing 2D CT image synthesis models lack. Our innovation is to develop a 3D U-Net architecture for the generator to improve shape and texture learning for PDAC tumors and pancreatic tissue. Our approach offers a promising path to tackle the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, this model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

翻译：胰腺导管腺癌（PDAC）是一项严峻的全球健康挑战，早期检测对提高五年生存率至关重要。近期医学影像与计算算法的进展为早期诊断提供了潜在解决方案。深度学习，特别是卷积神经网络（CNN）形式，已在医学图像分析任务（包括分类与分割）中展现出成功。然而，用于训练的临床数据有限仍构成重大障碍。数据增强、生成对抗网络（GAN）和交叉验证是应对这一局限并提升模型性能的潜在技术，但针对因肿瘤与背景组织高度异质性导致对比度尤其不足的3D PDAC，有效解决方案仍然稀缺。本研究开发了一种名为3DGAUnet的新型GAN模型，用于生成PDAC肿瘤和胰腺组织的逼真3D CT图像，该模型可生成现有2D CT图像合成模型所缺乏的层间连接数据。我们的创新在于开发了基于3D U-Net架构的生成器，以改进PDAC肿瘤与胰腺组织的形状与纹理学习。该方法为解决抗击PDAC所需的创新性协同方法的迫切需求提供了有前景的路径。该GAN模型的开发有望缓解数据稀缺问题，提升合成数据质量，从而促进深度学习模型的进展，提高PDAC肿瘤检测准确性与早期检测能力，可能对患者预后产生深远影响。此外，该模型具有适配其他实体肿瘤类型的潜力，从而在图像处理模型方面为医学影像领域做出重要贡献。