A Comparative Study of 2D and 3D U-Net-Based Architectures for Pancreas and Tumor Segmentation in CT Imaging

Pancreatic cancer ranks among the deadliest forms of cancer, largely due to its asymptomatic progression, late-stage diagnosis, and the pancreas’s deep-seated and anatomically complex location within the abdominal cavity. These factors collectively prevent early detection, which is critical for improving patient outcomes. Deep learning-based segmentation models, particularly those based on the U-Net architecture, have demonstrated strong potential for the automatic identification of pancreas and pancreatic tumors in computed tomography (CT) images. This study presents a comparative analysis of 2D and 3D U-Net architectures for the segmentation of the pancreas and its tumors. The results show that the 3D Res-U-Net achieved the best performance, with a Dice score of 0.81 for pancreas segmentation and 0.51 for tumor segmentation, outperforming both the 2D U-Net and the 3D U-Net. Additionally, inference time analysis for real-time clinical settings showed that the 2D U-Net enables the fastest predictions, whereas the 3D U-Net and 3D Res-U-Net require significantly higher computation, underscoring key trade-offs for clinical deployment. These findings offer valuable insights into model selection and optimization strategies for real-world clinical applications. The findings underscore the feasibility of developing efficient deep learning-based systems to support early diagnosis and personalized treatment planning for pancreatic cancer.