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Pancreatic ductal adenocarcinoma (PDAC) presents a critical global healthchallenge, and early detection is crucial for improving the 5-year survivalrate. Recent medical imaging and computational algorithm advances offerpotential solutions for early diagnosis. Deep learning, particularly in theform of convolutional neural networks (CNNs), has demonstrated success inmedical image analysis tasks, including classification and segmentation.However, the limited availability of clinical data for training purposescontinues to provide a significant obstacle. Data augmentation, generativeadversarial networks (GANs), and cross-validation are potential techniques toaddress this limitation and improve model performance, but effective solutionsare still rare for 3D PDAC, where contrast is especially poor owing to the highheterogeneity in both tumor and background tissues. In this study, we developeda new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images ofPDAC tumors and pancreatic tissue, which can generate the interslice connectiondata that the existing 2D CT image synthesis models lack. Our innovation is todevelop a 3D U-Net architecture for the generator to improve shape and texturelearning for PDAC tumors and pancreatic tissue. Our approach offers a promisingpath to tackle the urgent requirement for creative and synergistic methods tocombat PDAC. The development of this GAN-based model has the potential toalleviate data scarcity issues, elevate the quality of synthesized data, andthereby facilitate the progression of deep learning models to enhance theaccuracy and early detection of PDAC tumors, which could profoundly impactpatient outcomes. Furthermore, this model has the potential to be adapted toother types of solid tumors, hence making significant contributions to thefield of medical imaging in terms of image processing models.

cancers

3DGAUnet: 3D generative adversarial networks with a 3D U-Net based  generator to achieve the accurate and effective synthesis of clinical tumor  image data for pancreatic cancer