Automated Segmentation of the Median Nerve in the Carpal Tunnel using U-Net

Raymond T. Festen, Verena J.M.M. Schrier, Peter C. Amadio

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)1964-1969
Number of pages6
JournalUltrasound in Medicine and Biology
Issue number7
StatePublished - Jul 2021


  • Carpal tunnel
  • Median nerve
  • Segmentation
  • U-Net
  • Ultrasound

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biophysics
  • Acoustics and Ultrasonics


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