Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint. Issue 13 (3rd October 2017)

• Record Type: Journal Article
• Authors: Liukkonen, Mimmi K.; Mononen, Mika E.; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T.; Korhonen, Rami K.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 20:Issue 13(2017)
• Page Start: 1453
• Page End: 1463
• Publication Date: 2017-10-03
• Subjects: Cartilage -- finite element analysis -- image segmentation -- knee -- magnetic resonance imaging; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2017.1375477
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: 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.