Advertisement

Coronary Calcium Detection Based on Improved Deep Residual Network in Mimics

  • Chen DatongEmail author
  • Liang Minghui
  • Jin Cheng
  • Sun Yue
  • Xu Dongbin
  • Lin Yueming
Image & Signal Processing
  • 38 Downloads
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care

Abstract

Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algorithm firstly uses the aggregate channel feature model to preprocess the image to obtain the suspected calcium area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing calcium detection algorithms, and the detection efficiency is improved while ensuring the accuracy of calcium detection.

Keywords

Coronary calcium detection Single Shot MultiBox Detector Identity mapping Loss function Resnet network Suspected calcium area Aggregate channel feature 

Notes

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Mcclelland, R. L., Jorgensen, N. W., Budoff, M. J. et al., 10-year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: Derivation in the MESA (multi-ethnic study of atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study).[J]. J. Am. Coll. Cardiol. 66(15):1643–1653, 2015.CrossRefGoogle Scholar
  2. 2.
    Hecht, H. S., Coronary artery calcium scanning: Past, present, and future[J]. JACC Cardiovasc. Imaging 8(5):579–596, 2015.CrossRefGoogle Scholar
  3. 3.
    Rahhal, M. M., Bazi, Y., Alhichri, H. S. et al., Deep learning approach for active classification of electrocardiogram signals[J]. Inf. Sci. 345(345):340–354, 2016.CrossRefGoogle Scholar
  4. 4.
    Moeskops P, Moeskops P, Wolterink J M, et al. Deep learning for multi-task medical image segmentation in multiple modalities[C]. Medical Image Computing and Computer-Assisted Intervention: 478–486, 2016.Google Scholar
  5. 5.
    Wolterink, J. M., Leiner, T., Takx, R. A. et al., Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection[J]. IEEE Trans. Med. Imaging 34(9):1867–1878, 2015.CrossRefGoogle Scholar
  6. 6.
    Lloydjones, D. M., Coronary artery calcium scoring: Are we there yet?[J]. J. Am. Coll. Cardiol. 66(15):1654–1656, 2015.CrossRefGoogle Scholar
  7. 7.
    Araki, T., Ikeda, N., Dey, N. et al., Shape-based approach for coronary calcium lesion volume measurement on intravascular ultrasound imaging and its association with carotid intima-media thickness[J]. J. Ultrasound Med. 34(3):469–482, 2015.CrossRefGoogle Scholar
  8. 8.
    Chaikriangkrai, K., Valderrabano, M., Bala, S. K. et al., Abstract 491: Detection of subclinical coronary artery disease by calcium score in patients with atrial fibrillation: Potential clinical implications[J]. Arterioscler. Thromb. Vasc. Biol., 2015.Google Scholar
  9. 9.
    Vonder, M., Pelgrim, G. J., Huijsse, S. E. et al., Feasibility of spectral shaping for detection and quantification of coronary calcifications in ultra-low dose CT[J]. Eur. Radiol. 27(5):2047–2054, 2017.CrossRefGoogle Scholar
  10. 10.
    Qanadli, S. D., Qanadli, S. D., Jouannic, A. et al., CT attenuation values of blood and myocardium: Rationale for accurate coronary artery calcifications detection with multi-detector CT.[J]. PLoS ONE 10(4), 2015.Google Scholar
  11. 11.
    Antonopoulos, A. S., Sanna, F., Sabharwal, N. et al., Detecting human coronary inflammation by imaging perivascular fat[J]. Sci. Transl. Med. 9(398), 2017.Google Scholar
  12. 12.
    Greenland, P., Blaha, M. J., Budoff, M. J. et al., Coronary calcium score and cardiovascular risk[J]. J. Am. Coll. Cardiol. 72(4):434–447, 2018.CrossRefGoogle Scholar
  13. 13.
    Chang, H., Lin, F. Y., Lee, S. et al., Coronary atherosclerotic precursors of acute coronary syndromes[J]. J. Am. Coll. Cardiol. 71(22):2511–2522, 2018.CrossRefGoogle Scholar
  14. 14.
    Suzuki, K., Overview of deep learning in medical imaging[J]. Radiol. Phys. Technol. 10(3):257–273, 2017.CrossRefGoogle Scholar
  15. 15.
    Mahabadi, A. A., and Rassaf, T., Imaging of coronary inflammation for cardiovascular risk prediction[J]. Lancet 392(10151):894–896, 2018.CrossRefGoogle Scholar
  16. 16.
    Sun, J., Cerebral micro-bleeding identification based on nine-layer convolutional neural network with stochastic pooling, Concurrency and Computation: Practice and Experience, 2019. doi:  https://doi.org/10.1002/cpe.5130.
  17. 17.
    LeCun, Y., Bengio, Y., and Hinton, G., Deep learning[J]. nature 521(7553):436, 2015.CrossRefGoogle Scholar
  18. 18.
    Erhan, D., Bengio, Y., Courville, A. et al., Why does unsupervised pre-training help deep learning?[J]. J. Mach. Learn. Res. 11(Feb):625–660, 2010.Google Scholar
  19. 19.
    Liu, W., Anguelov, D., Erhan, D., et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Springer, Cham: 21–37, 2016.Google Scholar
  20. 20.
    Han, S., Mao, H., and Dally, W. J., Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding[J]. arXiv preprint arXiv:1510.00149, 2015.Google Scholar
  21. 21.
    Yang, B., Yan, J., Lei, Z., et al., Aggregate channel features for multi-view face detection[C]//Biometrics (IJCB), 2014 IEEE International Joint Conference on. IEEE, 1–8, 2014.Google Scholar
  22. 22.
    He, K., Gkioxari, G., Dollár, P., et al., Mask r-cnn[C]//Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2980–2988, 2017.Google Scholar
  23. 23.
    Szegedy, C., Ioffe, S., Vanhoucke, V., et al., Inception-v4, inception-resnet and the impact of residual connections on learning[C]//AAAI, 4:12, 2017.Google Scholar
  24. 24.
    Targ, S., Almeida, D., and Lyman, K., Resnet in Resnet: generalizing residual architectures[J]. arXiv preprint arXiv:1603.08029, 2016.Google Scholar
  25. 25.
    Sünderhauf, N., Shirazi, S., Dayoub, F., et al., On the performance of convnet features for place recognition[C]//Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE, 4297–4304, 2015.Google Scholar
  26. 26.
    Redmon, J., Divvala, S., Girshick, R., et al., You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788, 2016.Google Scholar
  27. 27.
    Ren, S., He, K., Girshick, R., et al., Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 91–99, 2015.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chen Datong
    • 1
    Email author
  • Liang Minghui
    • 1
  • Jin Cheng
    • 1
  • Sun Yue
    • 2
  • Xu Dongbin
    • 1
  • Lin Yueming
    • 1
  1. 1.School of Medical TechnologyQiqihar Medical UniversityQiqiharChina
  2. 2.Third Affiliated Hospital of Qiqihar Medical UniversityQiqiharChina

Personalised recommendations