Abstract
Lumbar spondylolisthesis (LS) is the anterior shift of one of the lower vertebrae about the subjacent vertebrae. There are several symptoms to define LS, and these symptoms are not detected in the early stages of LS. This leads to disease progress further without being identified. Thus, advanced treatment mechanisms are required to implement for diagnosing LS, which is crucial in terms of early diagnosis, rehabilitation, and treatment planning. Herein, a transfer learning-based CNN model is developed that uses only lumbar X-rays. The model was trained with 1922 images, and 187 images were used for validation. Later, the model was tested with 598 images. During training, the model extracts the region of interests (ROIs) via Yolov3, and then the ROIs are split into training and validation sets. Later, the ROIs are fed into the fine-tuned MobileNet CNN to accomplish the training. However, during testing, the images enter the model, and then they are classified as spondylolisthesis or normal. The end-to-end transfer learning-based CNN model reached the test accuracy of 99%, whereas the test sensitivity was 98% and the test specificity 99%. The performance results are encouraging and state that the model can be used in outpatient clinics where any experts are not present.
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References
Floman Y: Progression of lumbosacral isthmic spondylolisthesis in adults. Spine 25(3):342–347,2000
Gagnet P, Kern K, Andrews K, Elgafy H, Ebraheim N, et al: Spondylolysis and spondylolisthesis: A review of the literature. J Orthop 15(2):404–407,2018
Sutovsky J, Sutovska M, Kocmalova M, Kazimierova I, Pappova L, Benco M, Grendar M, Bredvold HH, Miklusica J, Franova S, et al: Degenerative lumbar spondylolisthesis. Biochemical aspects and evaluation of stabilization surgery extent in terms of adjacent segment disease theory. World Neurosurg 121:554–565,2019
Wiltse LL, Newman PH, Macnab I, et al : Classification of spondyloisis and spondylolisthesis. Clinical Orthopaedics and Related Research (1976-2007) 117:23–29,1976
Lasanianos NG, Kanakaris NK, Giannoudis PV, et al: Trauma and orthopaedic classifications: a comprehensive overview. Springer 2014
H.W. MEYERDING: Low backache and sciatic pain associated with spondylolisthesis and protruded intervertebral disc: incidence, significance, and treatment, JBJS 23(2), 461-470,1941
Aggarwal A, Rani A, Kumar M, et al: A robust method to authenticate car license plates using segmentation and roi based approach. Smart and Sustainable Built Environment, 2019
Kumar M, Srivastava S, Uddin N: Forgery detection using multiple light sources for synthetic images. Aust J Forensic Sci, 51(3):243–250,2019
Kumar M, Alshehri M, AlGhamdi R, Sharma P, Deep V: A de-ann inspired skin cancer detection approach using fuzzy c-means clustering. Mob Netw Appl 25:1319–1329,2020
Liao S, Zhan Y, Dong Z, Yan R, Gong L, Zhou XS, Salganicoff M, Fei J, et al: Automatic lumbar spondylolisthesis measurement in ct images. IEEE Trans Med Imaging 35(7):1658–1669,2016
Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS, et al: Robust automatic knee mr slice positioning through redundant and hierarchical anatomy detection. IEEE Trans Med Imaging 30(12):2087–2100,2011
Zhan Y, Dewan M, Harder M, Zhou XS, et al: Robust mr spine detection using hierarchical learning and local articulated model. In International conference on medical image computing and computer-assisted intervention, Springer, 2012, pp 141–148.
Cai Y, Leung S, Warrington J, Pandey S, Shmuilovich O, Li S, et al: Direct spondylolisthesis identification and measurement in mr/ct using detectors trained by articulated parameterized spine model. In Medical Imaging 2017: Image Processing, volume 10133, page 1013319. International Society for Optics and Photonics, 2017
Liu Y-Y, Xiao J, Yin X, Liu M-Y, Zhao J-H, Liu P, Dai F, et al: Clinical efficacy of bone cement-injectable cannulated pedicle screw short segment fixation for lumbar spondylolisthesis with osteoporosise. Sci Rep 10(1):1–9,2020
Zhao G, Liu G, Fang L, Tu B, Ghamisi P, et al: Multiple convolutional layers fusion framework for hyperspectral image classification. Neurocomputing 339:149–160,2019
LeCun Y, Bengio Y, Hinton G, et al: Deep learning. Nature, 521(7553):436–444,2015
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, et al: In 2009 IEEE conference on computer vision and pattern recognition 2009, pp 248–255
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J, et al: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312,2016
Krizhevsky A, Sutskever I, Hinton GE, et al: Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, 2012, pp 1097–1105
Wang S, He K, Nie D, Zhou S, Gao Y, Shen D, et al: Ct male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation. Med Image Anal 54:168–178,2019
Huang X, Sun W, Tseng T-LB, Li C, Qian W, et al: Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic ct scans using deep convolutional neural networks. Comput Med Imaging Graph 74:25–36,2019
Li F, Liu M, Alzheimer’s Disease Neuroimaging Initiative, et al: A hybrid convolutional and recurrent neural network for hippocampus analysis in alzheimer’s disease. J Neurosci Methods 323:108–118,2019
Li H, Jiang G, Zhang J, Wang R, Wang Z, Zheng W-S, Menze B, et al: Fully convolutional network ensembles for white matter hyperintensities segmentation in mr images. NeuroImage 183:650–665,2018
Chen C-H, Lee Y-W, Huang Y-S, Lan W-R, Chang R-F, Tu C-Y, Chen C-Y, Liao W-C, et al: Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network. Comput Meth Prog Bio 177:175–182,2019
Liu T, Guo Q, Lian C, Ren X, Liang S, Yu J, Niu L, Sun W, Shen D, et al: Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med Image Anal 58:101555,2019
Hu G, Yang X, Zhang Y, Wan M, et al: Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustainable Computing: Informatics and Systems 2019, p 100353
Üreten K, Erbay H, Maraş HH, et al: Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clinical rheumatology 39(4):969-974,2020
Fan J, Yang J, Wang Y, Yang S, Ai D, Huang Y, Song H, Hao A, Wang Y, et al: Multichannel fully convolutional network for coronary artery segmentation in x-ray angiograms. IEEE Access 6:44635–44643,2018
Goyal V, Singh G, Tiwari O, Punia S, Kumar M, et al: Intelligent skin cancer detection mobile application using convolution neural network. Advanced Research in Dynamical and Control Systems (JARCDS, IASR) 11(7(SI)):253–259,2019
Girshick R, Donahue J, Darrell T, Malik J, et al: Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition 2014, pp 580–587
Girshick R. Fast r-cnn: In Proceedings of the IEEE international conference on computer vision 2015, pp 1440–1448
Ren S, He K, Girshick R, Sun J, et al: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell 39(6):1137–1149,2016
Redmon J, Divvala S, Girshick R, Farhadi A, et al: You only look once: Unified, real-time object detection. arXiv preprint arXiv: 1506.02640, 2015
Cai Z, Vasconcelos N: In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp 6154–6162
Li J, Liang X, Shen S, Xu T, Feng J, Yan S, et al: Scale-aware fast r-cnn for pedestrian detection. IEEE Trans Multimedia 20(4):985–996,2017
Jiang H, Learned-Miller E: In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017, pp 650–657
Lan W, Dang J, Wang Y, Wang S, et al: In 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018, pp 1547–1551
Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018
Redmon J, Farhadi A. Yolo9000: Better, faster, stronger. arXiv preprint arXiv: 1612.08242, 2017
Shorten C, Khoshgoftaar TM: A survey on image data augmentation for deep learning. J Big Data 6(1):60,2019
Bloice MD, Roth PM, Holzinger A, et al: Biomedical image augmentation using augmentor. Bioinformatics 35(21):4522–4524,2019
Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M, et al: Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 30(4):477–486,2017
Nguyen K, Fookes C, Ross A, Sridharan S, et al: Iris recognition with off-the-shelf cnn features: A deep learning perspective. IEEE Access 6:18848–18855,2017
Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM, et al: Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298,2016
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H, et al: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017
Soekhoe D, Der Putten PV, Plaat A, et al: On the impact of data set size in transfer learning using deep neural networks. In International Symposium on Intelligent Data Analysis, Springer, 2016, pp 50–60
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Varçın, F., Erbay, H., Çetin, E. et al. End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays. J Digit Imaging 34, 85–95 (2021). https://doi.org/10.1007/s10278-020-00402-5
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DOI: https://doi.org/10.1007/s10278-020-00402-5