Artificial intelligence-driven three-dimensional modeling of biliary strictures using magnetic resonance cholangiopancreatography for quantitative assessment and stent planning

Table 2 Summary of segmentation and morphology-restoration experiments selected for the main manuscript

Analysis component	Detailed experiment summarized	Representative condition for main table	Primary metric	Result	Main interpretation
Bile duct segmentation	Swin-UNETR input resizing and window-size comparison	Input size 100 × 200 × 200; window size 96 × 96 × 96	Dice score	0.8113	Best result in the segmentation hyperparameter comparison; supports feasibility of whole biliary-tree segmentation
Bile duct segmentation	Extended normal-group segmentation experiment	Best-performing model in the extended normal-group setting	Dice score	0.8944	Higher overlap in non-strictured anatomy; useful for showing that stenotic signal loss is the harder condition
Morphology restoration	Loss-function comparison	DiceFocalLoss	Dice score	0.7531	Best restoration result; appropriate main-text evidence for imbalance-aware restoration of thin ductal structures
Morphology restoration	Training-iteration comparison	300000 iterations	Dice score	0.7508	Longer training improved reconstruction of interrupted duct segments
Morphology restoration	Lesion-weighting comparison	Lesion weight = 9	Dice score	0.7245	Targeted lesion weighting improved restoration around the synthetically interrupted segment
Three-dimensional reassembly and planning geometry	Representative qualitative reconstruction and centerline-guided mesh generation	Simple extrahepatic anatomy with limited branch overlap	Qualitative feasibility	Plausible restoration/candidate mesh	Shows technical feasibility for support path, approximate caliber, and curvature, but should not be interpreted as a validated stent design