Automatic retrieval of corresponding US views in longitudinal examinations
This addresses the need for more consistent muscle atrophy assessment in critically ill patients, though it is incremental as it builds on existing contrastive learning and segmentation methods.
The paper tackled the problem of high variability in manual ultrasound measurements of muscle atrophy in ICU patients by proposing a self-supervised contrastive learning approach to automatically retrieve similar ultrasound views across different scan times, achieving an AUC of 73.52% for view retrieval and a 5.7% error in cross-sectional area when combined with segmentation.
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.