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Updated: Jul 17, 2026
A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging
Published on: October 20, 2023
Automated MRI Liver Segmentation For Accurate Quantification Of Hepatic Steatosis
Xiaodie Wei1, Shi Qi2, Sitong Chen1
1Beijing Youan Hospital, Capital Medical University, Beijing, China (X.W., S.C., L.Q., X.W., J.Z., J.Z.).
Rationale And Objectives:
MRI-proton density fat fraction (MRI-PDFF) is widely applied in clinical practice for hepatic fat quantification. However, the conventional manual region of interest (ROI) method is time-consuming and operator-dependent, and the diagnostic accuracy of artificial intelligence (AI)-based models for hepatic steatosis assessment remains to be fully clarified. This study aimed to evaluate the diagnostic performance of an AI-based whole liver segmentation (WLS) model for histological hepatic steatosis grading, and to technically validate its segmentation performance and agreement with manual ROI-based PDFF measurement.
Materials And Methods:
A total of 538 adults who underwent MRI-PDFF examinations were enrolled. A VBB-Net segmentation model was developed in a training cohort of 372 patients (418 examinations). Validation was performed in a consecutive cohort of 166 adults who underwent liver biopsy for suspected metabolic dysfunction-associated steatotic liver disease (MASLD). Histological steatosis grading (S0-S3) was used as the reference standard.
Results:
The mean Dice coefficients were 0.94 ± 0.05 in the training cohort and 0.93 ± 0.04 in the validation cohort. Margins of error were 0.794% for AI-WLS-PDFF and 0.821% for ROI-PDFF. The AUROCs (95% CI) of AI-WLS-PDFF for diagnosing S0-S3 were 0.995 (95% CI: 0.989, 1.000), 0.951 (95% CI: 0.921, 0.980), and 0.928 (95% CI: 0.888, 0.968), respectively. AI-WLS-PDFF showed excellent agreement with ROI-PDFF (ICC, 0.996; 95% CI: 0.995, 0.997) with minimal bias (mean difference, -0.06%; 95% agreement limits: -1.59% to 1.47%).
Conclusion:
The AI-WLS-PDFF model showed high diagnostic performance for histological steatosis grading and excellent agreement with manual ROI-PDFF, supporting its potential as an automated approach for whole-liver PDFF quantification in patients with MASLD. The proposed cutoff values should be considered exploratory and require further validation.
