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MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging (2405.02784v1)

Published 5 May 2024 in eess.IV and cs.CV

Abstract: A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial correlation from the MR images. The performance of the proposed model was compared to existing state-of-the-art deep learning models for knee injury diagnosis using MRI. Knee MR scans of four different tissue contrasts from the Osteoarthritis Initiative and Multicenter Osteoarthritis Study databases were utilized in the study. Experimental results demonstrated the state-of-the-art performance of the proposed model on TKR prediction using MRI.

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References (16)
  1. J. H. Kellgren, J. Lawrence, et al., “Radiological assessment of osteo-arthrosis,” Ann Rheum Dis 16(4), 494–502 (1957).
  2. R. Altman, E. Asch, D. Bloch, et al., “Development of criteria for the classification and reporting of osteoarthritis: classification of osteoarthritis of the knee,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology 29(8), 1039–1049 (1986).
  3. D. T. Felson and Y. Zhang, “An update on the epidemiology of knee and hip osteoarthritis with a view to prevention,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology 41(8), 1343–1355 (1998).
  4. H. R. Rajamohan, T. Wang, K. Leung, et al., “Prediction of total knee replacement using deep learning analysis of knee mri,” Scientific reports 13(1), 6922 (2023).
  5. A. A. Tolpadi, J. J. Lee, V. Pedoia, et al., “Deep learning predicts total knee replacement from magnetic resonance images,” Scientific reports 10(1), 6371 (2020).
  6. K. Leung, B. Zhang, J. Tan, et al., “Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative,” Radiology 296(3), 584–593 (2020).
  7. C. Matsoukas, J. F. Haslum, M. Söderberg, et al., “Is it time to replace cnns with transformers for medical images?,” arXiv preprint arXiv:2108.09038 (2021).
  8. N. Bien, P. Rajpurkar, R. L. Ball, et al., “Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet,” PLoS medicine 15(11), e1002699 (2018).
  9. H. Touvron, M. Cord, M. Douze, et al., “Training data-efficient image transformers & distillation through attention,” in International conference on machine learning, 10347–10357, PMLR (2021).
  10. J. Deng, W. Dong, R. Socher, et al., “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition, 248–255, Ieee (2009).
  11. C. G. Peterfy, E. Schneider, and M. Nevitt, “The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee,” Osteoarthritis and cartilage 16(12), 1433–1441 (2008).
  12. N. A. Segal, M. C. Nevitt, K. D. Gross, et al., “The multicenter osteoarthritis study (most): opportunities for rehabilitation research,” PM & R: the journal of injury, function, and rehabilitation 5(8) (2013).
  13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems 25 (2012).
  14. S. Wang, Z. Zhuang, K. Xuan, et al., “3dmet: 3d medical image transformer for knee cartilage defect assessment,” in Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, 347–355, Springer (2021).
  15. R. R Core Team et al., “R: A language and environment for statistical computing,” (2013).
  16. S. Abnar and W. Zuidema, “Quantifying attention flow in transformers,” arXiv preprint arXiv:2005.00928 (2020).

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