Annals of Biomedical Engineering

, Volume 46, Issue 11, pp 1756–1767 | Cite as

Method for Segmentation of Knee Articular Cartilages Based on Contrast-Enhanced CT Images

  • Katariina A. H. MyllerEmail author
  • Juuso T. J. Honkanen
  • Jukka S. Jurvelin
  • Simo Saarakkala
  • Juha Töyräs
  • Sami P. Väänänen


Segmentation of contrast-enhanced computed tomography (CECT) images enables quantitative evaluation of morphology of articular cartilage as well as the significance of the lesions. Unfortunately, automatic segmentation methods for CECT images are currently lacking. Here, we introduce a semiautomated technique to segment articular cartilage from in vivo CECT images of human knee. The segmented cartilage geometries of nine knee joints, imaged using a clinical CT-scanner with an intra-articular contrast agent, were compared with manual segmentations from CT and magnetic resonance (MR) images. The Dice similarity coefficients (DSCs) between semiautomatic and manual CT segmentations were 0.79–0.83 and sensitivity and specificity values were also high (0.76–0.86). When comparing semiautomatic and manual CT segmentations, mean cartilage thicknesses agreed well (intraclass correlation coefficient = 0.85–0.93); the difference in thickness (mean ± SD) was 0.27 ± 0.03 mm. Differences in DSC, when MR segmentations were compared with manual and semiautomated CT segmentations, were statistically insignificant. Similarly, differences in volume were not statistically significant between manual and semiautomatic CT segmentations. Semiautomation decreased the segmentation time from 450 ± 190 to 42 ± 10 min per joint. The results reveal that the proposed technique is fast and reliable for segmentation of cartilage. Importantly, this is the first study presenting semiautomated segmentation of cartilage from CECT images of human knee joint with minimal user interaction.


Automation Contrast agent Knee joint Musculoskeletal Imaging 



The authors acknowledge the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (Projects 5041746, 5041757, and 5203101). Study is also supported by Doctoral Program in Science, Technology and Computing (SCITECO, University of Eastern Finland), Finnish Cultural Foundation, and Academy of Finland (Projects 269315 and 307932).

Conflict of interest

Authors declare no conflicts of interest.

Supplementary material

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Supplementary material 1 (DOCX 1481 kb)


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Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  1. 1.Department of Applied PhysicsUniversity of Eastern FinlandKuopioFinland
  2. 2.Diagnostic Imaging CenterKuopio University HospitalKuopioFinland
  3. 3.Center of OncologyKuopio University HospitalKuopioFinland
  4. 4.Research Unit of Medical Imaging, Physics and Technology, Faculty of MedicineUniversity of OuluOuluFinland
  5. 5.Department of Diagnostic RadiologyOulu University HospitalOuluFinland
  6. 6.Department of Orthopaedics, Traumatology and Hand SurgeryKuopio University HospitalKuopioFinland

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