by Jaeyoung Huh, Joo Hyeok Choi, Eun Sun Lee, Jong Chul Ye, Jeong Eun Lee, Hyun Jeong Park, Byung Ihn Choi Chronic liver disease (CLD) and subsequent liver cirrhosis (LC) are common causes of death and healthcare-related socio-economical costs worldwide. Ultrasound (US) is the first-line imaging modality for assessing the liver and associated hepatocellular carcinomas.
Poor quality liver US images caused by aging or inadequate management of US equipment, can pose significant challenges in both diagnosis and treatment. From this perspective, the aim of this study was to enhance and assess the image quality of liver US obtained from an older, lower-performing device using a deep learning approach.
A neural network based on a switchable cycle generative adversarial network (CycleGAN) was trained in an unsupervised learning setting, with low-quality images as inputs and high-quality images as targets. The study included consecutively acquired grey-scale liver US examinations from both a 12-year-old and a 4-year-old US device.
Images from the older device served as inputs, while images from the newer device were used as targets for the deep learning-based algorithm.
PLOS ONE (Medicine) published a clinical update in Research Highlights on 28 Apr 2026.
The item focuses on Image quality improvement of liver ultrasound using unsupervised deep learning.
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