From Simulation to Clinical Translation: A Deep Learning Framework for Pancreatic Tumor Segmentation With GUI Integration

Pancreatic tumor segmentation from computed tomography (CT) images remains a challenging task due to limited annotated datasets, pronounced anatomical variability, and the high computational demands of state-of-the-art deep learning models, which collectively hinder their routine clinical adoption. This study proposes a clinically oriented end-to-end framework that bridges methodological advances in deep learning with practical deployment by enabling adaptive segmentation under realistic data growth scenarios. Rather than introducing a novel segmentation architecture, the framework integrates existing convolutional and transformer-based models within a lightweight graphical user interface (GUI) and employs a recursive augmentation strategy as a simulation mechanism to emulate the incremental availability of annotated clinical data over time. Multiple candidate architectures were first evaluated using cross-validation, after which representative lightweight and high-capacity models were selected for recursive augmentation. The framework was subsequently evaluated using both CNN-based architectures, such as 3D U-Net, and transformer-based models, such as VT-UNet-B, on multiple large-scale public datasets. Across all experiments, the proposed recursive augmentation consistently improved segmentation performance relative to baseline training, yielding relative Dice Similarity Coefficient (DSC) gains in the range of approximately 4–11% before reaching architecture-dependent saturation. Lightweight CNNs exhibited earlier saturation with smaller but consistent improvements, whereas transformer-based models benefited more substantially from incremental data expansion. By embedding segmentation models into an interactive GUI that supports real-time visualization and expert-driven refinement, the proposed framework emphasizes deployment feasibility, adaptability, and continuous performance improvement. The results outline a practical pre-clinical pathway toward resource-aware pancreatic tumor segmentation in real-world healthcare environments.