Advertisement

Journal of Digital Imaging

, Volume 32, Issue 3, pp 513–520 | Cite as

Automatic Lumbar MRI Detection and Identification Based on Deep Learning

  • Yujing Zhou
  • Yuan Liu
  • Qian Chen
  • Guohua Gu
  • Xiubao SuiEmail author
Article

Abstract

The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image. In the convolutional neural network, a contrast object will not update during frames, which allows a fast speed and saves memory. Due to its distinctive shape, S1 is firstly detected and a rough region around it is extracted for searching for L1–L5. The results are evaluated with accuracy, precision, mean, and standard deviation (STD). Finally, our detection algorithm achieves the accuracy of 98.6% and the precision of 98.9%. Most failed results are involved with wrong S1 locations or missed L5. The study demonstrates that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images. It can be believed that our detection method will assist clinicians to raise working efficiency.

Keywords

Convolutional network Deep learning Lumbar detection The similarity function 

Notes

Funding Information

The research is supported by National Natural Science Foundation of China (Grant no. 11503010, 11773018), the Fundamental Research Funds for the Central Universities (Grant no. 30916015103), and the Qing Lan Project and Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging & Intelligence Sense (Grant no. 3091601410405).

References

  1. 1.
    Choi KC, Kim JS, Dong CL et al.: Percutaneous endoscopic lumbar discectomy: minimally invasive technique for multiple episodes of lumbar disc herniation. Bmc Musculoskelet Disord 18:329–324, 2017CrossRefGoogle Scholar
  2. 2.
    Li X, Dou Q, Hu S, Liu J, Kong Q, Zeng J, Song Y: Treatment of cauda equina syndrome caused by lumbar disc herniation with percutaneous endoscopic lumbar discectomy. Acta Neurologica Belgica 116(2):185–190, 2016CrossRefGoogle Scholar
  3. 3.
    Anitha H, Prabhu GK: Identification of Apical Vertebra for Grading of Idiopathic Scoliosis using Image Processing. J Digit Imaging 25(1):155–161, 2012CrossRefGoogle Scholar
  4. 4.
    Kumar S, Nayak KP, Hareesha KS: Improving Visibility of Stereo-Radiographic Spine Reconstruction with Geometric Inferences. J Digit Imaging 29(2):226–234, 2016CrossRefGoogle Scholar
  5. 5.
    Kumar VP, Thomas T: Automatic estimation of orientation and position of spine in digitized X-rays using mathematical morphology. J Digit Imaging 18(3):234–241, 2005CrossRefGoogle Scholar
  6. 6.
    Benjelloun M, Mahmoudi S: Spine localization in X-ray images using interest point detection. Journal of Digital Imaging 22(3):309–318, 2009CrossRefGoogle Scholar
  7. 7.
    Hinton GE, Salakhutdinov R: Reducing the dimensionality of data with neural networks. Science 313(5786):504–507, 2006CrossRefGoogle Scholar
  8. 8.
    Al Arif SMMR, Knapp K, Slabaugh G: Fully automatic cervical vertebrae segmentation framework for X-ray images. Comput Methods Prog Biomed 157:95–111, 2018CrossRefGoogle Scholar
  9. 9.
    Kim K, Lee S: Vertebrae localization in CT using both local and global symmetry features. Comput Med Imaging Graph 58:45–55, 2017CrossRefGoogle Scholar
  10. 10.
    Oktay AB, Akgul YS: Simultaneous Localization of Lumbar Vertebrae and Intervertebral Discs With SVM-Based MRF. IEEE Trans Biomed Eng 60(9):2375–2383, 2013CrossRefGoogle Scholar
  11. 11.
    Han Z, Wei B, Leung S et al.: Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning. Neuro informatics 1:1–13, 2018Google Scholar
  12. 12.
    Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P: A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 84(C):137–146, 2017CrossRefGoogle Scholar
  13. 13.
    Celik AN, Muneer T: Neural network based method for conversion of solar radiation data. Energy Convers Manag 67(1):117–124, 2013CrossRefGoogle Scholar
  14. 14.
    Forsberg D, Sjöblom E, Sunshine JL: Detection and labeling of vertebrae in mr images using deep learning with clinical annotations as training data. J Digit Imaging 30(4):1–7, 2017CrossRefGoogle Scholar
  15. 15.
    Gao J, Ling H, Hu W et al.: Transfer Learning Based Visual Tracking with Gaussian Processes Regression. Computer Vision – ECCV 2014. Springer International Publishing, 2014, pp. 188–203Google Scholar
  16. 16.
    Bertinetto L, Valmadre J, Henriques J F, et al. Fully-Convolutional Siamese Networks for Object Tracking. European Conference on Computer Vision – ECCV2016, 2016:850–865.Google Scholar
  17. 17.
    Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems. Curran Associates Inc. 60(2):1097–1105, 2012.Google Scholar
  18. 18.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L: ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015Google Scholar
  19. 19.
    Cai Y, Landis M, Laidley DT, Kornecki A, Lum A, Li S: Multimodal vertebrae recognition using transformed deep convolution network. Computerized Medical Imaging and Graphics 51:11–19, 2016CrossRefGoogle Scholar
  20. 20.
    Suzani A, Seitel A, Liu Y, et al. Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 9353:678–686, 2015.Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  • Yujing Zhou
    • 1
  • Yuan Liu
    • 1
  • Qian Chen
    • 1
  • Guohua Gu
    • 1
  • Xiubao Sui
    • 1
    Email author
  1. 1.The School of Electronic Engineering and Optoelectronic TechnologyNanjing University of Science and TechnologyNanjingChina

Personalised recommendations