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

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 sagitt...

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Bibliographic Details
Published in:Journal of ultrasound in medicine 2022-01, Vol.41 (1), p.175-184
Main Authors: Byra, Michal, Han, Aiguo, Boehringer, Andrew S., Zhang, Yingzhen N., O'Brien, William D., Erdman, John W., Loomba, Rohit, Sirlin, Claude B., Andre, Michael
Format: Article
Language:English
Subjects:
attention mechanism
convolutional neural networks
deep learning
Humans
Liver - diagnostic imaging
Machine Learning
Neural Networks, Computer
nonalcoholic fatty liver disease
proton density fat fraction
ultrasound images
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Summary: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.
ISSN:0278-4297
1550-9613
DOI:10.1002/jum.15693