  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.
