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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.).

Academic Radiology

|July 15, 2026

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An AI-based whole liver segmentation (WLS) model accurately quantifies hepatic fat using MRI-proton density fat fraction (MRI-PDFF) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). This automated approach shows high diagnostic performance for steatosis grading and excellent agreement with manual measurements.

Area of Science:

Radiology
Artificial Intelligence
Hepatology

Background:

Hepatic fat quantification using MRI-proton density fat fraction (MRI-PDFF) is crucial for diagnosing and managing liver conditions.
Manual region of interest (ROI) methods for MRI-PDFF are time-consuming and operator-dependent.
The diagnostic accuracy of AI models for hepatic steatosis assessment requires further clarification.

Purpose of the Study:

To evaluate the diagnostic performance of an AI-based whole liver segmentation (WLS) model for histological hepatic steatosis grading.
To technically validate the segmentation performance of the AI-WLS model.
To assess the agreement between AI-WLS-PDFF measurements and manual ROI-based PDFF measurements.

Main Methods:

A VBB-Net segmentation model was developed using a training cohort of 372 patients.

Keywords:

Hepatic steatosis Magnetic resonance imaging-proton density fat fraction Metabolic dysfunction-associated steatotic liver disease Whole liver segmentation

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Validation was performed in a cohort of 166 adults with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) who underwent liver biopsy.

Histological steatosis grading (S0-S3) served as the reference standard.

Main Results:

The AI-WLS model achieved high segmentation performance with mean Dice coefficients of 0.94 (training) and 0.93 (validation).
AI-WLS-PDFF demonstrated high diagnostic performance for steatosis grading (AUROCs ranging from 0.928 to 0.995).
Excellent agreement (ICC=0.996) and minimal bias were observed between AI-WLS-PDFF and manual ROI-PDFF.

Conclusions:

The AI-WLS-PDFF model offers high diagnostic accuracy for histological steatosis grading.
The AI-WLS model provides an automated and reliable alternative to manual ROI methods for whole-liver PDFF quantification.
This AI approach shows significant potential for managing patients with MASLD.