ArticleMagma (New York, N.Y.)2021
Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.
Article in Magma (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Measures of resting cartilage structure and composition in runners with and without knee osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Review
- BMI and Varus Malalignment Compound to Define a High-Risk Phenotype for Compartment-Specific Knee Osteoarthritis Progression.medRxiv : the preprint server for health sciences · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis.Frontiers in artificial intelligence · 2026Article
- Exercise-Induced Changes in Knee Cartilage In Vivo: Comparing MRI Sequences.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
- Improving Accuracy and Reproducibility of Cartilage TJournal of magnetic resonance imaging : JMRI · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
- Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage.Osteoarthritis and cartilage open · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.medRxiv : the preprint server for health sciences · 2024Article
- MRI-based TSkeletal radiology · 2023Review
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Investigating acute changes in osteoarthritic cartilage by integrating biomechanics and statistical shape models of bone: data from the osteoarthritis initiative.Magma (New York, N.Y.) · 2022Article
- Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee.Frontiers in bioengineering and biotechnology · 2022Article
- Latest advancements in imaging techniques in OA.Therapeutic advances in musculoskeletal disease · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectivesAccurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation. MATERIALS AND
methodsStage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using six fold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively.
resultsOn the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11 s per knee. DISCUSSION: The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.