Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks

Michal Byra, Aiguo Han, Andrew S. Boehringer, Yingzhen N. Zhang, William D. O'Brien, John W. Erdman, Rohit Loomba, Claude B. Sirlin, Michael Andre

Research output: Contribution to journalArticlepeer-review


Objectives: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney). Methods: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC). Results: The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%). Conclusion: Deep learning-based analysis of US images from different liver views can help assess liver fat.

Original languageEnglish (US)
Pages (from-to)175-184
Number of pages10
JournalJournal of Ultrasound in Medicine
Issue number1
StatePublished - Jan 2022


  • attention mechanism
  • convolutional neural networks
  • deep learning
  • nonalcoholic fatty liver disease
  • proton density fat fraction
  • ultrasound images

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this