Related Experiment Video
Updated: Sep 21, 2025
Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
Published on: October 20, 2023
Automated Measurement of Pancreatic Fat Deposition on Dixon MRI Using nnU-Net
Dingyi Lin1, Ziyan Wang1, Hong Li2
1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Background:
Pancreatic fat accumulation may cause or aggravate the process of acute pancreatitis, β-cell dysfunction, T2DM disease, and even be associated with pancreatic tumors. The pathophysiology of fatty pancreas remains overlooked and lacks effective imaging diagnostics.
Purpose:
To automatically measure the distribution of pancreatic fat deposition on Dixon MRI in multicenter/population datasets using nnU-Net models.
Study Type:
Retrospective.
Population:
A total of 176 obese/nonobese subjects (90 males, 86 females; mean age, 27.2 ± 19.7) were enrolled, including a training set (N = 132) and a testing set (N = 44).
Field Strength/Sequence:
A 3 T and 1.5 T/gradient echo T1 dual-echo Dixon.
Assessment:
The segmentation results of four types of nnU-Net models were compared using dice similarity coefficient (DSC), positive predicted value (PPV), and sensitivity. The ground truth was the manual delineation by two radiologists according to in-phase (IP) and opposed-phase (OP) images.
Statistical Tests:
The group difference of segmentation results of four models were assessed by the Kruskal-Wallis H test with Dunn-Bonferroni comparisons. The interobserver agreement of pancreatic fat fraction measurements across three observers and test-retest reliability of human and machine were assessed by intragroup correlation coefficient (ICC). P < 0.05 was considered statistically significant.
Results:
The three-dimensional (3D) dual-contrast model had significantly improved performance than 2D dual-contrast (DSC/sensitivity) and 3D one-contrast (IP) models (DSC/PPV/sensitivity) and had less errors than 3D one-contrast (OP) model according to higher DSC and PPV (not significant), with a mean DSC of 0.9158, PPV of 0.9105 and sensitivity of 0.9232 in the testing set. The test-retest ICC of this model was above 0.900 in all pancreatic regions, exceeded human.
Data Conclusion:
3D Dual-contrast nnU-Net aided segmentation of pancreas on Dixon images appears to be adaptable to multicenter/population datasets. It fully automates the assessment of pancreatic fat distribution and has high reliability.
Evidence Level:
3 TECHNICAL EFFICACY: Stage 3.
