Automatic retrieval of corresponding US views in longitudinal
  examinations
Hamideh Kerdegari Tran Huy Nhat Phung1 Van Hao Nguyen Thi Phuong Thao Truong Ngoc Minh Thu Le Thanh Phuong Le Thi Mai Thao Le Luigi Pisani Linda Denehy Vital Consortium Reza Razavi Louise Thwaites Sophie Yacoub Andrew P. King Alberto Gomez
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.