ArticleBMC musculoskeletal disorders2023
Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol.
Article in BMC musculoskeletal disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- 3D CSFA-UNet: a unified attention-driven deep learning framework for accurate knee MRI segmentation and osteoarthritis severity classification.Scientific reports · 2026Article
- Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging.Cureus · 2025Review
- Automated detection of wrist ganglia in MRI using convolutional neural networks.BMC musculoskeletal disorders · 2025Article
- Three-dimensional reconstruction of the knee joint based on automated 1.5T magnetic resonance image segmentation: A feasibility study.Journal of experimental orthopaedics · 2025Article
- Magnetic resonance imaging of cruciate ligament disorders: current updates.EFORT open reviews · 2025Review
- 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
- Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis.Frontiers in bioengineering and biotechnology · 2025Article
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage.Journal of orthopaedic surgery and research · 2024Article
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook.Diagnostics (Basel, Switzerland) · 2023Review
- Improving the Age Estimation Efficiency by Calculation of the Area Ratio Index Using Semi-Automatic Segmentation of Knee MRI Images.Biomedicines · 2023Article
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Authors and funding
6 authors.
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Abstract
backgroundTo study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness.
methodsThe sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The Dice similarity coefficient (DSC), Jaccard index and Hausdorff were used to compare the performance of the networks.
resultsCombining all sequences collectively performed significantly better than other alternatives. The following DSCs (±standard deviation) were obtained for the test dataset: Bone medulla 0.997 (±0.002), PCL 0.973 (±0.015), ACL 0.964 (±0.022), muscle 0.998 (±0.001), cartilage 0.966 (±0.018), bone cortex 0.980 (±0.010), arteries 0.943 (±0.038), collateral ligaments 0.919 (± 0.069), tendons 0.982 (±0.005), meniscus 0.955 (±0.032), adipose tissue 0.998 (±0.001), veins 0.980 (±0.010) and nerves 0.921 (±0.071). The deep learning network correctly identified the anterior cruciate ligament (ACL) tear of the left knee, thus indicating a future aid to orthopaedics.
conclusionsThe convolutional neural network proves highly capable of correctly labeling all anatomical structures of the knee joint when applied to 3D MR sequences. We have demonstrated that this deep learning model is capable of automatized segmentation that may give 3D models and discover pathology. Both useful for a preoperative evaluation.
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