ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2021
Automated magnetic resonance image segmentation of the anterior cruciate ligament.
Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Early MRI-based quantitative outcomes are associated with a positive functional performance trajectory from 6 to 24 months post-ACL surgery.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Trial
- Magnetic Resonance Imaging Assessment of Bridge-Enhanced ACL Restoration: An Imaging Atlas of Morphological Features and Healing Trajectories.Orthopaedic journal of sports medicine · 2026Article
- The application of transfer learning to T1ρ MRI tibiofemoral cartilage segmentation.Journal of biomechanics · 2026Article
- Advancement of an automatic segmentation pipeline for metallic artifact removal in post-surgical ACL MRI.Magnetic resonance imaging · 2025Article
- Magnetic resonance imaging of cruciate ligament disorders: current updates.EFORT open reviews · 2025Review
- [Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage.Osteoarthritis and cartilage open · 2025Article
- DualPlaqueNet with dual-branch structure and attention mechanism for carotid plaque semantic segmentation and size prediction.Frontiers in physiology · 2025Article
- Comprehensive evaluation of magnetic resonance imaging sequences for signal intensity based assessment of anterior cruciate ligament healing following surgical treatment.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2024Article
- LigaNET: A multi-modal deep learning approach to predict the risk of subsequent anterior cruciate ligament injury after surgery.medRxiv : the preprint server for health sciences · 2023Article
- Auto-segmentation of the tibia and femur from knee MR images via deep learning and its application to cartilage strain and recovery.Journal of biomechanics · 2023Article
- Automated segmentation of the healed anterior cruciate ligament from TJournal of orthopaedic research : official publication of the Orthopaedic Research Society · 2023Article
- Quantitative MRI Biomarkers to Predict Risk of Reinjury Within 2 Years After Bridge-Enhanced ACL Restoration.The American journal of sports medicine · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm.PloS one · 2023Article
- Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.Quantitative imaging in medicine and surgery · 2023Article
- Reproducibility and postacquisition correction methods for quantitative magnetic resonance imaging of the anterior cruciate ligament (ACL).Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2022Article
- Attachment Type, Thickness, and Volume of the Posterior Meniscofemoral Ligament and Meniscal Pathology.Journal of digital imaging · 2022Article
- Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.BMC musculoskeletal disorders · 2022Article
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5 authors.
Funding
Abstract
The objective of this study was to develop an automated segmentation method for the anterior cruciate ligament that is capable of facilitating quantitative assessments of the ligament in clinical and research settings. A modified U-Net fully convolutional network model was trained, validated, and tested on 246 Constructive Interference in Steady State magnetic resonance images of intact anterior cruciate ligaments. Overall model performance was assessed on the image set relative to an experienced (>5 years) "ground truth" segmenter in two domains: anatomical similarity and the accuracy of quantitative measurements (i.e., signal intensity and volume) obtained from the automated segmentation. To establish model reliability relative to manual segmentation, a subset of the imaging data was resegmented by the ground truth segmenter and two additional segmenters (A, 6 months and B, 2 years of experience), with their performance evaluated relative to the ground truth. The final model scored well on anatomical performance metrics (Dice coefficient = 0.84, precision = 0.82, and sensitivity = 0.85). The median signal intensities and volumes of the automated segmentations were not significantly different from ground truth (0.3% difference, p = .9; 2.3% difference, p = .08, respectively). When the model results were compared with the independent segmenters, the model predictions demonstrated greater median Dice coefficient (A = 0.73, p = .001; B = 0.77, p = NS) and sensitivity (A = 0.68, p = .001; B = 0.72, p = .003). The model performed equivalently well to retest segmentation by the ground truth segmenter on all measures. The quantitative measures extracted from the automated segmentation model did not differ from those of manual segmentation, enabling their use in quantitative magnetic resonance imaging pipelines to evaluate the anterior cruciate ligament.
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