ArticleEuropean journal of radiology open2026
Development and validation of a fully automated transformer-based 3D framework for pancreatic fat quantification in pancreatic steatosis.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Quantitative pancreatic MRI in diabetes mellitus: current advances and future directions.Frontiers in endocrinology · 2026Review
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5 authors.
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Abstract
Purpose: To develop and validate a fully automated TransUNet-based framework for 3D pancreatic segmentation and volumetric fat quantification on PDFF MRI. Specifically, to evaluate the model's performance against state-of-the-art CNN architectures and assess its clinical reproducibility compared to manual reference methods. Methods: This retrospective study involved 140 adults with metabolic dysfunction-associated steatotic liver disease who underwent 3.0 T multi-echo mDIXON MRI. A TransUNet-based model was developed using a 5-fold cross-validation approach, integrating convolutional and transformer layers to capture global and local features. Various architectures (UNet, nnUNet) and multiple input combinations of water, fat, and R2* maps were systematically compared. Segmentation performance was primarily assessed using the Dice Similarity Coefficient (DSC), with the Jaccard index, precision, recall, and 95th-percentile Hausdorff distance additionally used for network comparison. Agreement of pancreatic fat quantification across measurement methods was assessed using Bland-Altman analysis and intraclass correlation coefficients (ICC). Results: TransUNet achieved the highest segmentation accuracy (DSC: 85.46%, IQR: 80.76-86.24%), outperforming nnUNet (84.24%) and UNet (71.89%). Optimal performance was reached using water-only and fat-only input series. The AI-based volumetric method demonstrated strong agreement with manual whole-organ PDFF (r = 0.866, p < 0.001) with a minimal mean bias (1.20). Conversely, 2D ROI methods significantly underestimated pancreatic fat (6.08 ± 2.74%) compared to both manual (21.95 ± 6.14%) and AI-based (20.72 ± 6.13%) volumetric assessments (p < 0.001). Conclusion: TransUNet provides accurate, reproducible 3D pancreatic segmentation and fat quantification. By capturing the entire organ volume, this automated framework overcomes the sampling bias inherent in traditional 2D ROI methods, offering a fast and reliable biomarker for pancreatic steatosis.
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