ArticleQuantitative imaging in medicine and surgery2022
Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Artificial intelligence-based segmentation and quantitative assessment of knee cartilage in osteoarthritis: a systematic review and meta-analysis.International orthopaedics · 2026Review
- Multi-label Ensemble Model for Knee Joint Anatomy and Lesion Segmentation: Segmentation of Clinical Images With Ensembling, Morphology, and Attention (SCEMA).Magnetic resonance in medicine · 2026Article
- Automated knee MRI segmentation with KneeSeg-U for cartilage and bone extraction using an unsupervised deep learning framework.BMC musculoskeletal disorders · 2026Article
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- A review of 3D printed medical implant design.3D printing in medicine · 2026Article
- Automated 3D segmentation of rotator cuff muscle and fat from longitudinal CT for shoulder arthroplasty evaluation.Skeletal radiology · 2026Article
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis.Frontiers in bioengineering and biotechnology · 2025Article
- Imaging of knee osteoarthritis: a review of multimodal diagnostic approach.Quantitative imaging in medicine and surgery · 2023Review
- Deep learning applications in osteoarthritis imaging.Skeletal radiology · 2023Review
- Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022.Osteoarthritis imaging · 2023Article
- Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level.Bioengineering (Basel, Switzerland) · 2023Article
- Context-aware augmentation for liver lesion segmentation: shape uniformity, expansion limit and fusion strategy.Quantitative imaging in medicine and surgery · 2023Article
- Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.Journal of magnetic resonance imaging : JMRI · 2023Review
- Getting Cartilage Thickness Measurements Right: A Systematic Inter-Method Comparison Using MRI Data from the Osteoarthritis Initiative.Cartilage · 2023Article
- Early knee OA definition-what do we know at this stage? An imaging perspective.Therapeutic advances in musculoskeletal disease · 2023Review
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Authors and funding
9 authors.
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Abstract
Background: This study aimed to build a deep learning model to automatically segment heterogeneous clinical MRI scans by optimizing a pre-trained model built from a homogeneous research dataset with transfer learning. Methods: Conditional generative adversarial networks pretrained on the Osteoarthritis Initiative MR images was transferred to 30 sets of heterogenous MR images collected from clinical routines. Two trained radiologists manually segmented the 30 sets of clinical MR images for model training, validation and test. The model performance was compared to models trained from scratch with different datasets, as well as two radiologists. A 5-fold cross validation was performed. Results: The transfer learning model obtained an overall averaged Dice coefficient of 0.819, an averaged 95 percentile Hausdorff distance of 1.463 mm, and an averaged average symmetric surface distance of 0.350 mm on the 5 random holdout test sets. A 5-fold cross validation had a mean Dice coefficient of 0.801, mean 95 percentile Hausdorff distance of 1.746 mm, and mean average symmetric surface distance of 0.364 mm. It outperformed other models and performed similarly as the radiologists. Conclusions: A transfer learning model was able to automatically segment knee cartilage, with performance comparable to human, using heterogeneous clinical MR images with a small training data size. In addition, the model proved robust when tested through cross validation and on images from a different vendor. We found it feasible to perform fully automated cartilage segmentation of clinical knee MR images, which would facilitate the clinical application of quantitative MRI techniques and other prediction models for improved patient treatment planning.
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