Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Advances in Clinical RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Musculoskeletal trauma and artificial intelligence: current trends and projections.Skeletal Radiol. 2022; 51: 257-269
- Applications of artificial intelligence in musculoskeletal imaging: from the request to the report.Can Assoc Radiol J. 2021; 72: 45-59
- Clinical artificial intelligence applications: musculoskeletal.Radiol Clin North Am. 2021; 59: 1013-1026
- Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?.Skeletal Radiol. 2022; 51: 271-278
- Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.Eur Radiol. 2020; 30: 5525-5532
- Current applications and future directions of deep learning in musculoskeletal radiology.Skeletal Radiol. 2020; 49: 183-197
- Rapid Musculoskeletal MRI in 2021: value and Optimized Use of Widely Accessible Techniques.AJR Am J Roentgenol. 2021; 216: 704-717
- Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends.Semin Musculoskelet Radiol. 2019; 23: 304-311
- Semi-supervised deep learning for multi-tissue segmentation from multi-contrast MRI.J Signal Process Syst. 2022; 94: 497-510
- Capsules for biomedical image segmentation.Med Image Anal. 2021; 68: 101889
- The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset.Radiol Artif Intelligence. 2021; 3: e200078
- The baltimore longitudinal study of aging (BLSA): a 50-year-long journey and plans for the future.J Gerontol Ser A Biol Sci Med Sci. 2008; 63: 1416-1419
- Non-linear Gaussian filters performing edge preserving diffusion.in: Mustererkennung 1995. Springer, 1995: 538-545
- N4ITK: Improved N3 Bias correction.IEEE Trans Med Imaging. 2010; 29: 1310-1320
- New variants of a method of MRI scale standardization.IEEE Trans Med Imaging. 2000; 19: 143-150
- Image filtering via generalized scale.Med image Anal. 2008; 12: 87-98
- New methods of MR image intensity standardization via generalized scale.Med Phys. 2006; 33: 3426-3434
- The role of intensity standardization in medical image registration.Pattern Recognition Lett. 2010; 31: 315-323
- Hierarchical scale-based multiobject recognition of 3-D anatomical structures.IEEE Trans Med Imaging. 2011; 31: 777-789
- Joint solution for PET image segmentation, denoising, and partial volume correction.Med Image Anal. 2018; 46: 229-243
- Numerical tissue characterization in MS via standardization of the MR image intensity scale.J Magn Reson Imaging. 2000; 12: 715-721
- The Baltimore Longitudinal Study of Aging (BLSA): a 50-year-long journey and plans for the future.J Gerontol A Biol Sci Med Sci. 2008 Dec; 63: 1416-1419
- Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.Magn Reson Med. 2018; 79: 2379-2391
- Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection.Radiology. 2018; 289: 160-169
- Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort.Osteoarthritis Cartilage. 2019; 27: 1002-1010
- Segnet: a deep convolutional encoder-decoder architecture for image segmentation.IEEE Trans Pattern Anal Mach Intell. 2017; 39: 2481-2495
Heimann T, Morrison BJ, Styner MA, Niethammer M, Warfield S. Segmentation of knee images: a grand challenge. Paper presented at: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic2010.
- Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative.Osteoarthritis Cartilage. 2018; 26: 680-688
- Segmentation of the proximal femur from MR images using deep convolutional neural networks.Sci Rep. 2018; 8: 1-14
- Deep convolutional neural network for segmentation of knee joint anatomy.Magn Reson Med. 2018; 80: 2759-2770
Mortazi A, Karim R, Rhode K, Burt J, Bagci U. CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2017. pp. 377-385.
- Super-resolution musculoskeletal MRI using deep learning.Magn Reson Med. 2018; 80: 2139-2154
Irmakci I, Anwar SM, Torigian DA, Bagci U. Deep learning for musculoskeletal image analysis. Paper presented at: 2019 53rd Asilomar Conference on Signals, Systems, and Computers2019.
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?.Skeletal Radiol. 2022; 51: 293-304
- Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence.Semin Musculoskelet Radiol. 2020; 24: 38-49
Article info
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
