Akademik Veri Yönetim Sistemi
IEEE Access, cilt.14, ss.26767-26783, 2026 (SCI-Expanded, Scopus)
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.