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Computer-based quantification of the mean Achilles tendon thickness in ultrasound images: effect of tendinosis
  1. R Syha1,
  2. M Peters2,
  3. H Birnesser2,
  4. A Niess3,
  5. A Hirschmueller1,
  6. H-H Dickhuth1,
  7. M Sandrock3
  1. 1
    Freiburg University Hospital, Centre for Internal Medicine, Department for Rehabilitative and Preventative Sports Medicine, Freiburg, Germany
  2. 2
    Freiburg University Hospitals, Department for Traumatology and Orthopaedics, Division for Sportorthopaedics, Freiburg, Germany
  3. 3
    Tuebingen University Hospitals, Centre for Internal Medicine, Department for Sports Medicine, Tuebingen, Germany
  1. Markus Sandrock, Tuebingen University Hospital, Centre for Internal Medicine, Department for Sports Medicine, Silcherstreet.5, 72076 Tuebingen, Germany; markus.sandrock{at}


Background: B-mode measurement of the sagital diameter of the Achilles tendon based on a manual tracing (MT) procedure is partly dependent on the subjectivity of the reader. The aim of this study is to establish a standardised automatic procedure to differentiate between normal and chronically degenerated tendons. For this comparison, the tracing results of the tendon boundaries of an automatic identification (AI) process, already established with the detection of intima–media thickness, are compared with computer-assisted MT.

Methods: The detection of the tendon boundaries was performed in 115 ultrasound images including the cranial border of the calcaneal tuberosity. The measured section (starting point 4 cm away from the anterior boundary of the calcaneal tuberosity) amounted to 3 cm, and was divided into three sub-segments (1 cm each). Intra- and inter-reader/observer variability for mean and maximum Achilles tendon thickness (ATT) with AI and MT were evaluated. A normal group and a group with clinically diagnosed chronic tendon degeneration had mean and maximum ATT readings compared.

Results: Using MT, the intra- and inter-reader variability was 3.0% and 6.8%, respectively, using AI the variability was 1.6% and 3.9%, respectively. Mean and maximum ATT were measured systematically lower by AI compared to MT in all regions by 0.4 mm. The AI procedure was most accurate in the second segment. The mean ATT and maximum ATT were correctly detected in 93.9% and 96.6% of the images.

Conclusion: The AI procedure detected the ATT with a high level of precision in all three segments. The most robust measurement was reached in the second segment. It eliminates most of the inter-/intra-reader variability in ATT measurement using MT. We suggest this new method could be a new gold standard for quantification of chronic disorder in Achilles tendons.

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  • Abbreviations:
    automatic identification
    Achilles tendon
    Achilles tendon thickness
    manual tracing