Nerve area and motion in carpal tunnel syndrome (CTS) are currently under investigation in terms of prognostic potential. Therefore, there is increasing interest in non-invasive measurement of the nerve using ultrasound. Manual segmentation is time consuming and subject to inter-rater variation, providing an opportunity for automation. Dynamic ultrasound images (n = 5560) of carpal tunnels from 99 clinically diagnosed CTS patients were used to train a U-Net-shaped neural network. The best results from the U-Net were achieved with a location primer as initial region of interest for the segmentations during finger flexion (Dice coefficient = 0.88). This is comparable to the manual Dice measure of 0.92 and higher than the resulting automated Dice measure of wrist flexion (0.81). Although there is a dependency on image quality, a trained U-Net can reliably be used in the assessment of ultrasound-acquired median nerve size and mobility, considerably decreasing manual effort.
|Number of pages||6|
|Journal||Ultrasound in Medicine and Biology|
|Publication status||Published - 1 Jul 2021|
Bibliographical noteFunding Information:
The authors acknowledge the U.S. National Institutes of Health (NIH) for providing funding for this work. The NIH was not involved in the design of the study, collection of data, manuscript writing or decision to publish.
© 2021 World Federation for Ultrasound in Medicine & Biology