ArticleMagnetic resonance in medicine2020
Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
- Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review.Arthroscopy, sports medicine, and rehabilitation · 2025Review
- Automated detection and segmentation of chondromalacia patella on axial knee MRI using YOLOv11 and a custom CNN: a deep learning-based diagnostic model.BMC musculoskeletal disorders · 2025Article
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.Quantitative imaging in medicine and surgery · 2024Article
- Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- Magnetic resonance imaging for assessment of the quadriceps tendon cross-sectional area as an adjunctive diagnostic parameter in patients with patellofemoral pain syndrome.Joint diseases and related surgery · 2023Article
- Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.Journal of magnetic resonance imaging : JMRI · 2023Review
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Influence of the Fluoroscopy Setting towards the Patient When Identifying the MPFL Insertion Point.Diagnostics (Basel, Switzerland) · 2022Article
- Future Directions in Patellofemoral Imaging and 3D Modeling.Current reviews in musculoskeletal medicine · 2022Review
- A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning.Diagnostics (Basel, Switzerland) · 2022Review
- Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.Nature reviews. Rheumatology · 2022Review
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?Skeletal radiology · 2022Review
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Emergence of Deep Learning in Knee Osteoarthritis Diagnosis.Computational intelligence and neuroscience · 2021Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeOur clinical understanding of the relationship between 3D bone morphology and knee osteoarthritis, as well as our ability to investigate potential causative factors of osteoarthritis, has been hampered by the time-intensive nature of manually segmenting bone from MR images. Thus, we aim to develop and validate a fully automated deep learning framework for segmenting the patella and distal femur cortex, in both adults and actively growing adolescents.
methodsData from 93 subjects, obtained from on institutional review board-approved protocol, formed the study database. 3D sagittal gradient recalled echo and gradient recalled echo with fat saturation images and manual models of the outer cortex were available for 86 femurs and 90 patellae. A deep-learning-based 2D holistically nested network (HNN) architecture was developed to automatically segment the patella and distal femur using both single (sagittal, uniplanar) and 3 cardinal plane (triplanar) methodologies. Errors in the surface-to-surface distances and the Dice coefficient were the primary measures used to quantitatively evaluate segmentation accuracy using a 9-fold cross-validation.
resultsAverage absolute errors for segmenting both the patella and femur were 0.33 mm. The Dice coefficients were 97% and 94% for the femur and patella. The uniplanar, relative to the triplanar, methodology produced slightly superior segmentation. Neither the presence of active growth plates nor pathology influenced segmentation accuracy.
conclusionThe proposed HNN with multi-feature architecture provides a fully automatic technique capable of delineating the often indistinct interfaces between the bone and other joint structures with an accuracy better than nearly all other techniques presented previously, even when active growth plates are present.
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