Related Experiment Video
Updated: Jul 6, 2026
07:33
In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
Published on: May 5, 2023
Enhanced Magnetic Resonance Imaging-Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative
Jun Young Park1, Ji-Hoon Nam2,3,4, Shakhboz Abdigapporov3
1Department of Orthopaedic Surgery, Yonsei University College of Medicine, Yongin Severance Hospital, Yongin-si, Gyeonggi-do, Republic of Korea.
JMIR Medical Informatics
|March 2, 2026
Summary
A new deep learning model, Swin-UNet conditional generative adversarial network (cGAN), accurately segments knee cartilage in MRI scans. This advanced framework improves boundary accuracy and generalizability for better surgical planning in total knee arthroplasty.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Orthopedic Surgery
Background:
- Accurate knee cartilage segmentation from MRI is vital for diagnosing knee osteoarthritis and planning surgeries.
- Manual segmentation is time-consuming, and CT-based systems lack cartilage visualization capabilities.
Purpose of the Study:
- To develop and evaluate a deep learning framework, Swin-UNet conditional generative adversarial network (cGAN), for automatic femoral and tibial cartilage segmentation in MRI.
- To compare its performance against conventional UNet, UNet cGAN, and Swin-UNet baseline models.
Main Methods:
- Utilized a dataset of 232 knee MRI scans for quantitative experiments.
- Compared Swin-UNet cGAN against UNet, UNet cGAN, and Swin-UNet using Dice similarity coefficient, mean intersection over union, and surface distance metrics.
- Evaluated the model's performance on an external validation dataset.
Main Results:
- Swin-UNet cGAN achieved superior Dice similarity coefficient and intersection over union scores for both femoral and tibial cartilage segmentation.
- The model demonstrated significantly improved performance in distance metrics for tibial cartilage and comparable results for femoral cartilage.
- Consistently high segmentation accuracy was observed on both internal test and external validation datasets.
Conclusions:
- The Swin-UNet cGAN offers more accurate knee cartilage segmentation, especially in boundary definition, compared to baseline models.
- The model exhibits promising generalizability across different cohorts, addressing limitations of CT-based systems by enabling cartilage visualization.
- This MRI-based deep learning approach has the potential to enhance surgical precision and patient outcomes in total knee arthroplasty.
