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With the substantial diversity in population demographics, such asdifferences in age and body composition, the volumetric morphology of pancreasvaries greatly, resulting in distinctive variations in shape and appearance.Such variations increase the difficulty at generalizing population-widepancreas features. A volumetric spatial reference is needed to adapt themorphological variability for organ-specific analysis. Here, we proposed ahigh-resolution computed tomography (CT) atlas framework specifically optimizedfor the pancreas organ across multi-contrast CT. We introduce a deeplearning-based pre-processing technique to extract the abdominal region ofinterests (ROIs) and leverage a hierarchical registration pipeline to align thepancreas anatomy across populations. Briefly, DEEDs affine and non-rigidregistration are performed to transfer patient abdominal volumes to a fixedhigh-resolution atlas template. To generate and evaluate the pancreas atlastemplate, multi-contrast modality CT scans of 443 subjects (without reportedhistory of pancreatic disease, age: 15-50 years old) are processed. Comparingwith different registration state-of-the-art tools, the combination of DEEDsaffine and non-rigid registration achieves the best performance for thepancreas label transfer across all contrast phases. We further perform externalevaluation with another research cohort of 100 de-identified portal venousscans with 13 organs labeled, having the best label transfer performance of0.504 Dice score in unsupervised setting. The qualitative representation (e.g.,average mapping) of each phase creates a clear boundary of pancreas and itsdistinctive contrast appearance. The deformation surface renderings acrossscales (e.g., small to large volume) further illustrate the generalizability ofthe proposed atlas template.

Multi-Contrast Computed Tomography Atlas of Healthy Pancreas