Learning Structured Deviation from Normal Anatomy for Pancreatic Tumor Segmentation

Pancreatic tumor segmentation on CT remains challenging because CT-only predictions may fail to reliably distinguish the pancreatic tumor from normal tissue. We investigate whether complementary evidence can be obtained from how a patient's anatomy deviates from plausible normal states. To this end, we introduce a Deviation-Aware Residual Adaptation (DARA) model, which consists of Star-chain Anatomy Graph (SAG) and Bounded Residual Reconciliation (BRR). SAG learns a multi-prototype normal anatomy universe from normal reference data and matches each patient's anatomy to class-compatible normal prototypes. SAG measures structural anatomical deviation between the patient and the normal reference and projects structural anatomical deviation into image space as a deviation field that complements the CT-only prediction. BRR trains a refiner using CT-only and SAG-aware out-of-fold (OOF) predictions and applies its bounded residual correction to the CT-only tumor logit. Effectiveness of the proposed DARA model has been validated by using three widely-used datasets (PanTSMini, MSD07, and PANORAMA) with five metrics (DSC, IoU, NSD, HD95, Recall). Experimental results show that the proposed model outperforms all competing methods in DSC, IoU, and NSD. In particular, 3.96%, 3.86%, 0.66% DSC gains can be achieved compared with the second-best method in each dataset respectively. These results suggest that learning what normal anatomy should look like provides a complementary cue to recognizing what is abnormal.