ArticleMagma (New York, N.Y.)2021
Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.
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 28 papers.
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Who cites it
28 citing papers in PubMed, 46 citations in OpenAlex.
- OpenMSK: A modular open-source framework for comprehensive analysis of imaging biomarkers in knee osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Clinical validation of fully automated (peri-)articular tissue analysis for assessing osteoarthritis progression: A narrative review.Osteoarthritis and cartilage open · 2026Review
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- A memory based model for cartilage and meniscus segmentation in 3D knee MRI.Scientific reports · 2025Article
- Cartilage loss is greater in knees with virtual joint replacement status than in matched controls without.Osteoarthritis and cartilage open · 2025Article
- Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation - Data from the FNIH biomarker cohort.Osteoarthritis and cartilage open · 2025Article
- A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis - Method development and validation.Osteoarthritis and cartilage open · 2025Article
- Multifaceted imaging strategies for clinical trials of knee osteoarthritis-a tightly interlinked value and precision chain.Skeletal radiology · 2025Review
- Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.Journal of personalized medicine · 2025Review
- Clinical validation of fully automated cartilage transverse relaxation time (T2) and thickness analysis using quantitative DESS magnetic resonance imaging.Magma (New York, N.Y.) · 2025Article
- Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.Skeletal radiology · 2025Observational
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
- Reproducibility of Quantitative Double-Echo Steady-State TJournal of magnetic resonance imaging : JMRI · 2025Article
- Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods.PeerJ. Computer science · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.medRxiv : the preprint server for health sciences · 2024Article
- The design of a sample rapid magnetic resonance imaging (MRI) acquisition protocol supporting assessment of multiple articular tissues and pathologies in knee osteoarthritis.Osteoarthritis and cartilage open · 2024Article
- Clinical validation of fully automated laminar knee cartilage transverse relaxation time (T2) analysis in anterior cruciate ligament (ACL)-injured knees- on behalf of the osteoarthritis (OA)-Bio consortium.Quantitative imaging in medicine and surgery · 2024Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- Deep learning applications in osteoarthritis imaging.Skeletal radiology · 2023Review
- Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.Journal of magnetic resonance imaging : JMRI · 2023Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 4 countries.
Funding
Abstract
objectiveTo evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI.
methods2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%).
resultsAutomated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test-retest reproducibility over 1 year (RMSCV% 1.0-4.5%) as manual measurements (RMSCV% 0.5-2.5%). DISCUSSION: The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.
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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.