Abstract
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.
Original language | English (US) |
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Pages (from-to) | 1964-1969 |
Number of pages | 6 |
Journal | Ultrasound in Medicine and Biology |
Volume | 47 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2021 |
Keywords
- Carpal tunnel
- Median nerve
- Segmentation
- U-Net
- Ultrasound
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Biophysics
- Acoustics and Ultrasonics