Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver

Trong N. Nguyen, Anthony S. Podkowa, Trevor H. Park, Rita J. Miller, Minh N. Do, Michael L. Oelze

Research output: Contribution to journalArticlepeer-review

Abstract

Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl4. The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9-4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radiofrequency (RF) signals from the livers using either QUS or a 1-D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN outputs the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combined with a support vector machine in differentiating between low and high lipid liver levels (i.e., accuracies of 74% versus 59% on the testing data). Therefore, although the CNN did not provide a physical interpretation of the tissue properties (e.g., attenuation of the medium or scatterer properties) the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

Original languageEnglish (US)
Pages (from-to)556-568
Number of pages13
JournalUltrasound in Medicine and Biology
Volume47
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Liver disease
  • Machine learning
  • Quantitative ultrasound

ASJC Scopus subject areas

  • Biophysics
  • Radiological and Ultrasound Technology
  • Acoustics and Ultrasonics

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