University of Oulu

Mimmi K. Liukkonen, Mika E. Mononen, Petri Tanska, Simo Saarakkala, Miika T. Nieminen & Rami K. Korhonen (2017) Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint, Computer Methods in Biomechanics and Biomedical Engineering, 20:13, 1453-1463, DOI: 10.1080/10255842.2017.1375477

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint

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Author: Liukkonen, Mimmi K.1,2; Mononen, Mika E.1; Tanska, Petri1;
Organizations: 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
3Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
4Medical Research Center Oulu, University of Oulu, Oulu, Finland
5Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link:
Language: English
Published: Informa, 2017
Publish Date: 2019-11-14


Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

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Series: Computer methods in biomechanics and biomedical engineering
ISSN: 1025-5842
ISSN-E: 1476-8259
ISSN-L: 1025-5842
Volume: 20
Issue: 13
Pages: 1453 - 1463
DOI: 10.1080/10255842.2017.1375477
Type of Publication: A1 Journal article – refereed
Field of Science: 3111 Biomedicine
Funding: The research leading to these results has received funding from the Academy of Finland (grant 286526, 305138), Sigrid Juselius Foundation, the strategic funding of the University of Eastern Finland and Kuopio University Hospital (no. 931053), Kuopio University Hospital (VTR grant 5041752), Oulu University Hospital (VTR grant K33745) and Doctoral Programme in Science, Technology and Computing, University of Eastern Finland. CSC – IT Center for Science Ltd, Finland is acknowledged for providing the finite-element modeling software.
Copyright information: © Taylor & Francis 2017. This is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering on 12.9.2017, available online: