References
- D. J. Hunter, Y. Q. Zhang, J. B. Niu, X. Tu, S. Amin, M. Clancy, D. T. Felson, "The association of meniscal pathologic changes with cartilage loss in symptomatic knee osteoarthritis," Arthritis & Rheumatism, Vol. 54, No. 3, pp. 795-801, 2006. https://doi.org/10.1002/art.21724
- M. Roth, W. Wirth, K. Emmanuel, A. G. Culvenor, F. Eckstein, "The contribution of 3D quantitative meniscal and cartilage measures to variation in normal radiographic joint space width- Data from the Osteoarthritis Initiative healthy reference cohort," European journal of radiology, Vol. 87, pp. 90-98, 2017. https://doi.org/10.1016/j.ejrad.2016.12.009
- M. S. Swanson, J. W. Prescott, T. M. Best, K. Powell, R. D. Jackson, F. Haq, and M. N. Gurcan, "Semi-automated segmentation to assess the lateral meniscus in nonnal and osteoarthritic knees," Osteoarthritis and cartilage, Vol. 18, No. 3, pp. 344-353, 2010. https://doi.org/10.1016/j.joca.2009.10.004
- M. S. M. Swamy, and M. S. Holi, "Knee Joint Menisci Visualization and Detection of Tears by Image Processing," Computing, Communication and Applications, 2012 International Conference on. IEEE, pp. 1-5, 2012.
- J. Fripp, P. Bourgeat, C. Engstrom, S. Ourselin, S. Crozier, and O. Salvado, "Automated Segmentation of the Menisci from MR Images, " Biomedical Imaging, pp. 210-513, 2009.
- M. Kim, J. Yoo, and H. Hong, "Automatic Segmentation of the meniscus based on Active Shape Model in MR Images through Interpolated Shape Information," Journal of Korean Institute of Information Scientists and Engineers, Vol. 16, No. 11 , pp. 1096-1100, 2010.
- A. Paproki, C. Engstrom, S. Chandra, A. Neubert, J. Fripp, and S. Crozier, "Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance unages - data from the Osteoarthritis Initiative," Osteoarthritis Cartilage, Vol. 22, No. 9, pp. 1259-1270, 2014. https://doi.org/10.1016/j.joca.2014.06.029
- S. Kim, H. Kim, H. Hong, J. Wang, "Automatic Segmentation of Meniscus using Position Estimation and Multi-atlas based Locally-weighted Voting in Knee MR Images," KllSE, Proceedings of the Korea Computer Congress, pp. 1412-1414, 2018.
- E. B. Dam, M. Lillholm, J. Marques, and M. Nielsen, "Automatic segmentation of high-and low-field knee MRls using knee image quantification with data from the osteoarthritis initiative," Journal of Medical Imaging, 2015.
- K. Zhang, W. Lu, and P. Marziliano, "The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images," Machine vision and applications, Vol. 24, No. 7, pp. 1459-1472, 2013. https://doi.org/10.1007/s00138-012-0466-9
- B. Nonnan, V. Pedoia, and S. Majumdar. "Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry," Radiology, Ahead of Print, 2018.
- A. Raj, S. Vishwanathan, B. Ajani, K. Krishnan, and H. Agarwal, "Automatic knee cartilage segmentation using fully volumetric convolutional neural networks for evaluation of osteoarthritis," Biomedical Imaging, pp. 851-854, 2018.
- H. Kim, H. Kim, H. Lee, H. Hong, "Automatic Segmentation of Femoral Cartilage in Knee MR Images using Multi-atlas-based Locally-weighted Voting," Journal of Korean Institute of Information Scientists and Engineers, Vol. 43, No. 8, pp. 889-877, 2016.