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


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.


Convolutional network Deep learning Lumbar detection The similarity function 


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).


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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

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