ArticleOsteoarthritis and cartilage open2025
A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis - Method development and validation.
Article in Osteoarthritis and cartilage open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 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
- Advances in cartilage imaging techniques.Nature reviews. Rheumatology · 2026Review
- 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
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Denuded areas of subchondral bone (dAB) pose a challenge for fully automated segmentation of articular cartilage and subchondral bone in knees with severe radiographic osteoarthritis using convolutional neural networks (CNNs). Here we propose an automated post-processing relying on a selection-based multi-atlas registration for reconstructing the total area of subchondral bone (tAB) to overcome this issue. We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this novel methodology. Design: CNN-based models were trained using manual cartilage segmentations from sagittal DESS and coronal FLASH MRI of knees with radiographic (KLG2-4) or severe radiographic osteoarthritis (KLG4 only). These were then applied to KLG4 test knees with manual cartilage segmentations. Automated post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations, particularly for dABs. The agreement and accuracy of automated cartilage analysis were evaluated using Dice Similarity Coefficients (DSC) and Bland-Altman analyses; sensitivity to one-year change was assessed using the standardized response mean (SRM). Results: Stronger agreement (DSC 0.80 ± 0.07 to 0.89 ± 0.05) and lower systematic offsets for cartilage thickness (1.2 %-8.4 %) and tAB area (-0.4 %-4.3 %) were observed for CNNs trained on KLG2-4 rather than KLG4 knees; overall, results were superior to those without registration-based post-processing. Sensitivity to change was greatest for manual segmentation of DESS (SRM ≥ -0.69; automated: ≥-0.56) and for automated segmentation of FLASH (≥-0.74; manual ≥-0.44). Conclusion: CNN-based segmentation combined with registration-based post-processing for accurate delineation of tABs/dABs substantially improves fully-automated (longitudinal) analysis of cartilage and subchondral bone morphology in knees with severe radiographic osteoarthritis.
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