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.
This work is supported by the National Natural Science Foundation of China (U19B2030, 61976167, 62101416, 11727813) and the Natural Science Basic Research Program of Shaanxi (2022JQ-708).
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Zhang, Y. et al. (2024). Self Supervised Temporal Ultrasound Reconstruction for Muscle Atrophy Evaluation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_22
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DOI: https://doi.org/10.1007/978-981-99-8546-3_22
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