Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images

Overview

Journal J Arthroplasty

Specialty Orthopedics

Date 2019 Aug 26

PMID 31445869

Citations 19

Authors

Romil F Shah

Alejandro M Martinez

Valentina Pedoia

Sharmila Majumdar

Thomas P Vail

Stefano A Bini

Affiliations

Soon will be listed here.

Abstract

Background: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement of ACT on magnetic resonance imaging (MRI) (2) use ML to provide reference data on ACT in healthy knees, and (3) identify whether demographic variables impact these results.

Methods: Patients recruited into the Osteoarthritis Initiative with a radiographic Kellgren-Lawrence grade of 0 or 1 with 3D double-echo steady-state MRIs were included and their gender, age, and body mass index were collected. Using a validated ML algorithm, 2 orthogonal points on each femoral condyle were identified (distal and posterior) and ACT was measured on each MRI. Site-specific ACT was compared using paired t-tests, and multivariate regression was used to investigate the risk-adjusted effect of each demographic variable on ACT.

Results: A total of 3910 MRI were included. The average femoral ACT was 2.34 mm (standard deviation, 0.71; 95% confidence interval, 0.95-3.73). In multivariate analysis, distal-medial (-0.17 mm) and distal-lateral cartilage (-0.32 mm) were found to be thinner than posterior-lateral cartilage, while posterior-medial cartilage was found to be thicker (0.21 mm). In addition, female sex was found to negatively impact cartilage thickness (OR, -0.36; all values: P < .001).

Conclusion: ML was effectively used to automate the segmentation and measurement of cartilage thickness on a large number of MRIs of healthy knees to provide normative data on the variation in ACT in this population. We further report patient variables that can influence ACT. Further validation will determine whether this technique represents a powerful new tool for tracking the impact of medical intervention on the progression of articular cartilage degeneration.

Citing Articles

Advances and Challenges in the Pursuit of Disease-Modifying Osteoarthritis Drugs: A Review of 2010-2024 Clinical Trials.

Brandt M, Malone J, Kean T Biomedicines. 2025; 13(2).

PMID: 40002768 PMC: 11853018. DOI: 10.3390/biomedicines13020355.

Is Native Joint Line More Accurately Restored with Robotic Assisted Total Knee Arthroplasty than with Conventional Instruments?.

Bhor P, Pawar S, Kutumbe D, Vatkar A, Kale S, Jagtap R J Orthop Case Rep. 2025; 15(2):233-238.

PMID: 39957945 PMC: 11823860. DOI: 10.13107/jocr.2025.v15.i02.5294.

Evaluation of the Femoral Condyle Radius of Curvature at the Chondral Surface Shows Significant Correlation With the Anterior-Posterior Length.

Jerban S, Tabbaa S, Caldwell 3rd P, Jones K, Bugbee W, Crawford D Cartilage. 2025; :19476035251314109.

PMID: 39881445 PMC: 11780605. DOI: 10.1177/19476035251314109.

An MRI-Derived Formula for Estimating the Native Joint Line Position in the Presence of Distal Femoral Bone Loss.

Rao R, Lim A, Ho J, Ong L, Kamaruddin F Cureus. 2024; 16(11):e73707.

PMID: 39677165 PMC: 11646141. DOI: 10.7759/cureus.73707.

Unbiased Method to Determine Articular Cartilage Thickness Using a Three-Dimensional Model Derived from Laser Scanning: Demonstration on the Distal Femur.

Campanelli V, Hull M Bioengineering (Basel). 2024; 11(11).

PMID: 39593779 PMC: 11591366. DOI: 10.3390/bioengineering11111118.

References

Buck R, Wyman B, Le Graverand M, Hudelmaier M, Wirth W, Eckstein F . Osteoarthritis may not be a one-way-road of cartilage loss--comparison of spatial patterns of cartilage change between osteoarthritic and healthy knees. Osteoarthritis Cartilage. 2009; 18(3):329-35. DOI: 10.1016/j.joca.2009.11.009. View

Lakhani P, Sundaram B . Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017; 284(2):574-582. DOI: 10.1148/radiol.2017162326. View

Blazek K, Favre J, Asay J, Erhart-Hledik J, Andriacchi T . Age and obesity alter the relationship between femoral articular cartilage thickness and ambulatory loads in individuals without osteoarthritis. J Orthop Res. 2013; 32(3):394-402. DOI: 10.1002/jor.22530. View

Schmitz R, Wang H, Polprasert D, Kraft R, Pietrosimone B . Evaluation of knee cartilage thickness: A comparison between ultrasound and magnetic resonance imaging methods. Knee. 2016; 24(2):217-223. DOI: 10.1016/j.knee.2016.10.004. View

Ahn C, Bui T, Lee Y, Shin J, Park H . Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative. Biomed Eng Online. 2016; 15(1):99. PMC: 4997678. DOI: 10.1186/s12938-016-0225-7. View

Eckstein F, Cicuttini F, Raynauld J, Waterton J, Peterfy C . Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment. Osteoarthritis Cartilage. 2006; 14 Suppl A:A46-75. DOI: 10.1016/j.joca.2006.02.026. View

Pradsgaard D, Fiirgaard B, Spannow A, Heuck C, Herlin T . Cartilage thickness of the knee joint in juvenile idiopathic arthritis: comparative assessment by ultrasonography and magnetic resonance imaging. J Rheumatol. 2014; 42(3):534-40. DOI: 10.3899/jrheum.140162. View

Stammberger T, Eckstein F, Englmeier K, Reiser M . Determination of 3D cartilage thickness data from MR imaging: computational method and reproducibility in the living. Magn Reson Med. 1999; 41(3):529-36. DOI: 10.1002/(sici)1522-2594(199903)41:3<529::aid-mrm15>3.0.co;2-z. View

ESCAMILLA R . Knee biomechanics of the dynamic squat exercise. Med Sci Sports Exerc. 2001; 33(1):127-41. DOI: 10.1097/00005768-200101000-00020. View

10.

Frobell R, Nevitt M, Hudelmaier M, Wirth W, Wyman B, Benichou O . Femorotibial subchondral bone area and regional cartilage thickness: a cross-sectional description in healthy reference cases and various radiographic stages of osteoarthritis in 1,003 knees from the Osteoarthritis Initiative. Arthritis Care Res (Hoboken). 2010; 62(11):1612-23. DOI: 10.1002/acr.20262. View

11.

Eckstein F, Winzheimer M, Westhoff J, Schnier M, Haubner M, Englmeier K . Quantitative relationships of normal cartilage volumes of the human knee joint--assessment by magnetic resonance imaging. Anat Embryol (Berl). 1998; 197(5):383-90. DOI: 10.1007/s004290050149. View

12.

Omoumi P, Michoux N, Roemer F, Thienpont E, Vande Berg B . Cartilage thickness at the posterior medial femoral condyle is increased in femorotibial knee osteoarthritis: a cross-sectional CT arthrography study (Part 2). Osteoarthritis Cartilage. 2014; 23(2):224-31. DOI: 10.1016/j.joca.2014.08.017. View

13.

Adam C, Eckstein F, Milz S, Putz R . The distribution of cartilage thickness within the joints of the lower limb of elderly individuals. J Anat. 1998; 193 ( Pt 2):203-14. PMC: 1467840. DOI: 10.1046/j.1469-7580.1998.19320203.x. View

14.

Ding C, Cicuttini F, Scott F, Cooley H, Jones G . Association between age and knee structural change: a cross sectional MRI based study. Ann Rheum Dis. 2005; 64(4):549-55. PMC: 1755432. DOI: 10.1136/ard.2004.023069. View

15.

Lukasz S, Muhlbauer R, Faber S, Englmeier K, Reiser M, Eckstein F . [Sex-specific analysis of cartilage volume in the knee joint--a quantitative MRI-based study]. Ann Anat. 1998; 180(6):487-93. View

16.

Iwaki H, Pinskerova V, Freeman M . Tibiofemoral movement 1: the shapes and relative movements of the femur and tibia in the unloaded cadaver knee. J Bone Joint Surg Br. 2000; 82(8):1189-95. DOI: 10.1302/0301-620x.82b8.10717. View

17.

Argentieri E, Sturnick D, Desarno M, Gardner-Morse M, Slauterbeck J, Johnson R . Changes to the articular cartilage thickness profile of the tibia following anterior cruciate ligament injury. Osteoarthritis Cartilage. 2014; 22(10):1453-60. DOI: 10.1016/j.joca.2014.06.025. View

18.

Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R . Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med. 2017; 79(4):2379-2391. PMC: 6271435. DOI: 10.1002/mrm.26841. View

19.

Wirth W, Hunter D, Nevitt M, Sharma L, Kwoh C, Ladel C . Predictive and concurrent validity of cartilage thickness change as a marker of knee osteoarthritis progression: data from the Osteoarthritis Initiative. Osteoarthritis Cartilage. 2017; 25(12):2063-2071. PMC: 5688009. DOI: 10.1016/j.joca.2017.08.005. View

20.

Faber S, Eckstein F, Lukasz S, Muhlbauer R, Hohe J, Englmeier K . Gender differences in knee joint cartilage thickness, volume and articular surface areas: assessment with quantitative three-dimensional MR imaging. Skeletal Radiol. 2001; 30(3):144-50. DOI: 10.1007/s002560000320. View