Skeletal muscle atrophy is a common occurrence in critically ill patients in
the intensive care unit (ICU) who spend long periods in bed. Muscle mass must
be recovered through physiotherapy before patient discharge and ultrasound
imaging is frequently used to assess the recovery process by measuring the
muscle size over time. However, these manual measurements are subject to large
variability, particularly since the scans are typically acquired on different
days and potentially by different operators. In this paper, we propose a
self-supervised contrastive learning approach to automatically retrieve similar
ultrasound muscle views at different scan times. Three different models were
compared using data from 67 patients acquired in the ICU. Results indicate that
our contrastive model outperformed a supervised baseline model in the task of
view retrieval with an AUC of 73.52% and when combined with an automatic
segmentation model achieved 5.7%+/-0.24% error in cross-sectional area.
Furthermore, a user study survey confirmed the efficacy of our model for muscle
view retrieval.

本文提出了一种自我监督的对比学习方法，用于自动检索不同时期的相似肌肉超声视图，结果表明与监督学习模型相比，我们的对比学习模型在视图检索任务中表现更好，结合自动分割模型后的交叉面积误差在 5.7%+/-0.24% 之间。此外，用户研究调查证实了我们的模型在肌肉视图检索方面的有效性。