Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection

Abstract

Accurate and fully automated pancreas segmentation is critical for advancing imaging biomarkers in early pancreatic cancer detection and for biomarker discovery in endocrine and exocrine pancreatic diseases. We developed and evaluated a deep learning (DL)-based convolutional neural network (CNN) for automated pancreas segmentation using the largest single-institution dataset to date (n = 3031 CTs). Ground truth segmentations were performed by radiologists, which were used to train a 3D nnU-Net model through five-fold cross-validation, generating an ensemble of top-performing models. To assess generalizability, the model was externally validated on the multi-institutional AbdomenCT-1K dataset (n = 585), for which volumetric segmentations were newly generated by expert radiologists and will be made publicly available. In the test subset (n = 452), the CNN achieved a mean Dice Similarity Coefficient (DSC) of 0.94 (SD 0.05), demonstrating high spatial concordance with radiologist-annotated volumes (Concordance Correlation Coefficient [CCC]: 0.95). On the AbdomenCT-1K dataset, the model achieved a DSC of 0.96 (SD 0.04) and a CCC of 0.98, confirming its robustness across diverse imaging conditions. The proposed DL model establishes new performance benchmarks for fully automated pancreas segmentation, offering a scalable and generalizable solution for large-scale imaging biomarker research and clinical translation.

Keywords: Artificial intelligence; Computed tomography; Pancreas; Volumetric segmentation.

Fig. 1

Study design and structure of datasets.

Fig. 2

Comparison of pancreas segmentations by radiologists and AI model: (a) Portal venous phase axial slice of a patient depicting the pancreas, (b, c) Radiologists’ and model-predicted segmentation respectively, (d) Overlaid segmentation where the overlap, over- and under-segmentation are represented by blue, red and green, respectively. The Dice similarity coefficient (DSC) for this case was 0.97, indicating a high level of agreement. The model’s segmentation demonstrates excellent fidelity despite the pancreas’s lobulated contours and curvature. Three-dimensional views of the radiologists’ and model-predicted segmentations are presented in panels (e) and (f), respectively.

Fig. 3

Comparison of radiologists and AI model-predicted pancreas volumes for internal test subset: (a) Bland–Altman analyses show that the mean pancreas volume difference between reference and model-predicted segmentation 2.41 cc (limits of agreement 1.96 SD of 16.3 cc to − 11.5 cc). (b) The plot shows high correlation between reference and model-predicted volumes with the concordance correlation coefficient (CCC) of 0.95.

Fig. 4

Comparison of radiologists and AI model-predicted pancreas volumes for abdomen CT-1 K multi-institutional public dataset: (a) Bland–Altman analyses show that the mean pancreas volume difference between reference and model-predicted segmentation 1.85 cc (limits of agreement 1.96 SD of 9.8 cc to − 6.2 cc). (b) The plot shows high correlation between reference and model-predicted volumes with the concordance correlation coefficient (CCC) of 0.98.

Fig. 5

Representative case highlighting segmentation discrepancies on a public dataset: (a) Axial portal venous phase CT demonstrating a diffusely fatty pancreas. (b) Ground truth segmentation from the NIH-PCT dataset, showing marked under-segmentation of the pancreatic parenchyma. (c) Segmentation predicted by our AI model, demonstrating more accurate and anatomically complete delineation of the pancreas.

Fig. 6

AI-predicted pancreas segmentations in representative high- and low-performing cases: (a, b) Axial portal venous phase CT slices from two patients, corresponding to a high-performing case (a) and a low-performing case (b). (c, d) Ground truth segmentations provided by expert radiologists. (e, f) AI-generated segmentations. (g, h) Overlay maps highlighting agreement (blue), over-segmentation (red), and under-segmentation (green) between ground truth and AI predictions. The first case demonstrated excellent concordance, with a Dice Similarity Coefficient (DSC) of 0.99. The second case exhibited pronounced segmentation error (DSC = 0.36), associated with substantial paucity of peripancreatic fat and a diffusely atrophic pancreas.