European journal of radiology · 2025
Deep-learning based automated pancreas segmentation on CT scans of chronic pancreatitis patients.
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Nalliah Surenth, Mark Esben Bolvig, Liedenbaum Marjolein Henrieke, Mosegaard Mille Kristence Lillien, Hellström Tobias, Hodneland Erlend, Haldorsen Ingfrid Helene Salvesen, Engjom Trond, Drewes Asbjørn Mohr, Olesen Søren Schou, Frøkjær Jens Brøndum
Аннотация
OBJECTIVES: This study aimed to develop an artificial intelligence (AI)-based segmentation model for accurate delineation of the complex pancreas in patients with chronic pancreatitis (CP) using computer tomography (CT) scans obtained during routine clinical work-up. Validation was performed with internal and external test datasets. A secondary objective was to evaluate the impact of visceral fat area (at the third lumbar level), pancreas volume, and CT parameters on model performance.
METHODS: This multicenter study included 550 retrospectively collected CT scans from Aalborg (n = 373; 224 CP, 150 healthy subjects) and Bergen Hospitals (n = 97 CP), and an online dataset from the National Institutes of Health (NIH) (n = 80, healthy subjects). The Aalborg dataset was divided into a training cohort (n = 326) and an internal test set (n = 47), while the Bergen and NIH datasets served as external test sets. The AI model employed the nnU-Net architecture, with performance evaluated using the Sørensen-Dice index. Correlations with visceral fat, pancreas volume, and CT parameters were assessed.
RESULTS: The pancreas segmentation AI model achieved a Dice score of 0.85 ± 0.08 on the Aalborg test set, 0.79 ± 0.19 on the Bergen dataset, and 0.79 ± 0.18 on the NIH dataset. Visceral fat and pancreas volume positively correlated with Dice scores (r = 0.45 and r = 0.53, both p < 0.0001), while CT parameters had no significant impact (all p-values > 0.07).
CONCLUSION: The AI model demonstrated high accuracy and robustness in pancreas segmentation of both CP patients and healthy subjects, and across diverse sites and scanners, suggesting its potential for clinical application.
METHODS: This multicenter study included 550 retrospectively collected CT scans from Aalborg (n = 373; 224 CP, 150 healthy subjects) and Bergen Hospitals (n = 97 CP), and an online dataset from the National Institutes of Health (NIH) (n = 80, healthy subjects). The Aalborg dataset was divided into a training cohort (n = 326) and an internal test set (n = 47), while the Bergen and NIH datasets served as external test sets. The AI model employed the nnU-Net architecture, with performance evaluated using the Sørensen-Dice index. Correlations with visceral fat, pancreas volume, and CT parameters were assessed.
RESULTS: The pancreas segmentation AI model achieved a Dice score of 0.85 ± 0.08 on the Aalborg test set, 0.79 ± 0.19 on the Bergen dataset, and 0.79 ± 0.18 on the NIH dataset. Visceral fat and pancreas volume positively correlated with Dice scores (r = 0.45 and r = 0.53, both p < 0.0001), while CT parameters had no significant impact (all p-values > 0.07).
CONCLUSION: The AI model demonstrated high accuracy and robustness in pancreas segmentation of both CP patients and healthy subjects, and across diverse sites and scanners, suggesting its potential for clinical application.