Automatic Lumbar MRI Detection and Identification Based on Deep Learning
- 325 Downloads
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.
KeywordsConvolutional network Deep learning Lumbar detection The similarity function
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).
- 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
- 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.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.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.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
- 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