(1) Comprehensive encoder benchmark: Systematically compares representative CNN, Transformer, and hybrid CNN–Transformer encoders for pancreas segmentation under a unified experimental setting to identify the relative strengths of different feature-extraction paradigms.
(2) Progressive segmentation-pipeline evaluation: Evaluates four segmentation pipelines designed to address key challenges in pancreas CT segmentation, including severe foreground–background imbalance, low boundary contrast, and substantial anatomical variability.
(3) Rigorous performance–efficiency analysis: Evaluates all architecture–pipeline combinations using identical five-fold cross-validation and analyzes segmentation performance alongside model size and inference time, identifying the combination that provides the best balance between accuracy and computational efficiency.
