Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model

Xiongfeng Tang; Deming Guo; Aie Liu; Dijia Wu; Jianhua Liu; Nannan Xu; Yanguo Qin

doi:10.12659/MSM.936733

14 June 2022 : Clinical Research

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model

Xiongfeng Tang^1ABCEF, Deming Guo^1BF, Aie Liu^2ACDE, Dijia Wu^2ADE, Jianhua Liu^3BCD, Nannan Xu^3BDF, Yanguo Qin^4ACDG*

DOI: 10.12659/MSM.936733

Med Sci Monit 2022; 28:e936733

Authors information Article notes Copyright and License information

Background Material and Methods Results Discussion Conclusions References

Related articles Order reprints Share article Share by email

View HTML version

Figure 5 The illustration of knee joint space width and the location of minimal joint space width (mJSW) after automatic segmentation and 3D reconstruction. (A) Diagram of mJSW; (B) detail view. For this example, the location of mJSW is the lateral-anterior compartment, which may indicate there was an apparent lateral-anterior symptom for this patient. (Made by Itk-Snap 3.6.0).

Back to the Article

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model

Your Privacy