Abstract
Muscle atrophy is a widespread disease that can reduce quality of life and increase morbidity and mortality. The development of non-invasive method to evaluate muscle atrophy is of great practical value. However, obtaining accurate criteria for the evaluation of muscle atrophy under non-invasive conditions is extremely difficult. This paper proposes a self-supervised temporal ultrasound reconstruction method based on masked autoencoder to explore the dynamic process of muscle atrophy. A score-position embedding is designed to realize the quantitative evaluation of muscle atrophy. Ultrasound images of the hind limb muscle of six macaque monkeys were acquired consecutively during 38 days of head-down bed rest experiments. Given an ultrasound image sequence, an asymmetric encoder-decoder structure is used to reconstruct the randomly masked images for the purpose of modelling the dynamic muscle atrophy process. We demonstrate the feasibility of using the position indicator as muscle atrophy score, which can be used to predict the degree of muscle atrophy. This study achieves the quantitative evaluation of muscle atrophy in the absence of accurate evaluation criteria for muscle atrophy.
Kaitai Guo
Assistant Professor
My research interests include broad-spectrum substance identification, microwave and infrared imaging, and system simulation and evaluation.
Yang Zheng
Assistant Professor
My research interests include human behaviour analysis for intelligent diagnosis of developmental coordination disorder, aritifical intelligence, and computer vision.
Jimin Liang
Professor of Electronic Engineering
My research interests include artificial intelligence and computer vision.