ArticleJournal of digital imaging2021
A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 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
- Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.Journal of imaging informatics in medicine · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.Plant phenomics (Washington, D.C.) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age. The pathology of OA disease is still unclear, and there are no effective interventions that can completely alter the OA disease process. Magnetic resonance (MR) image evaluation is sensitive for depicting early changes of knee OA, and therefore important for early clinical intervention for relieving the symptom. Automated cartilage segmentation based on MR images is a vital step in experimental longitudinal studies to follow-up the patients and prospectively define a new quantitative marker from OA progression. In this paper, we develop a deep learning-based coarse-to-fine approach for automated knee bone, cartilage, and meniscus segmentation with high computational efficiency. The proposed method is evaluated using two-fold cross-validation on 507 MR volumes (81,120 slices) with OA from the Osteoarthritis Initiative (OAI)
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Registered trials
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