ArticleJournal of experimental orthopaedics2025
Three-dimensional reconstruction of the knee joint based on automated 1.5T magnetic resonance image segmentation: A feasibility study.
Article in Journal of experimental orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Subchondral bone abnormalities in the knee: imaging techniques, regional anatomy, and pathology.Skeletal radiology · 2026Review
- Use of Patient-Specific 3D Models in Paediatric Surgery: Effect on Communication and Surgical Management.Journal of imaging · 2026Article
- 3D ZTE MRI Versus 3D CT for Measurement of Glenoid Bone Loss: An Analysis of Agreement, Accuracy, and Cost Comparison.Orthopaedic journal of sports medicine · 2026Article
- Three-dimensional reconstruction of the knee joint based on automated 1.5T magnetic resonance image segmentation: A feasibility study.Journal of experimental orthopaedics · 2025Article
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8 authors.
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Abstract
Purpose: To validate the accuracy of three-dimensional (3D) bone and cartilage reconstructions of the distal femur and proximal tibia derived from 1.5 Tesla magnetic resonance imaging (MRI), using fully automated and semi-automated segmentation methods, compared to surface laser scanning (LS) as the reference standard. Methods: Eleven fresh-frozen cadaveric knees were imaged using a 1.5 T MRI scanner. Manual (MS), fully automated (A), and semi-automated (SA) segmentations were performed to generate 3D models of the distal femur and proximal tibia. A transformer-based deep learning model (UNet-R) was used for automated segmentation. Laser surface scanning provided high-resolution ground-truth 3D models. Point-to-surface distances between MRI-based and LS-derived models were calculated to assess reconstruction accuracy. Bland-Altman analyses were performed to compare segmentation methods. Time to generate 3D models was recorded for each method. Results: The mean absolute point-to-surface distance for femoral models was 1.19 mm (±0.42) for MRI A, 1.05 mm (±0.09) for MRI SA, and 0.99 mm (±0.08) for MRI MS. For tibial models, the corresponding values were 1.54 mm (±1.02), 1.03 mm (±0.17), and 0.93 mm (±0.14), respectively. MRI A showed larger variability, which required manual correction. Time analysis revealed significant efficiency gains: 27 s for MRI A, 1520 s for MRI SA, and 14,191 s for MRI MS ( Conclusions: MRI-based 3D reconstructions of the knee using a 1.5 T system and semi-automated segmentation achieved sub-millimetre accuracy comparable to manual segmentation and significantly outperformed fully automated models in precision, while substantially reducing segmentation time. These findings support the integration of AI-assisted 3D reconstruction into preoperative planning workflows for knee ligament surgery, offering a reliable, radiation-free alternative to CT-based modelling. Level of Evidence: Level IV, controlled laboratory study.
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