Skip to main content
Log in

Faster R-CNN, fourth-order partial differential equation and global-local active contour model (FPDE-GLACM) for plaque segmentation in IV-OCT image

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The accurate segmentation of plaque in intravascular optical coherence tomography (IV-OCT) image plays an important role in coronary atherosclerotic heart disease (CAD) diagnosis. To effectively provide information of coronary artery stenosis, we propose a novel hybrid framework which includes the faster R-CNN, fourth-order partial differential equation and global-local active contour model (FPDE-GLACM). This framework can efficiently detect and segment the plaque area in Speckle noise-contaminated IV-OCT images. We first detect plaque area by faster R-CNN and set bounding-box as the initial contour for active contour model. And then we minimize the joint energy functional of PDE-GLACM part to achieve the segmentation and denoising of IV-OCT images by gradient descent and finite difference scheme. Specifically, by using the Gaussian image minus original image to get the edge guide image, GLACM part obtains accurate plaque segmentation results. We perform experiments on 5000 IV-OCT images and set clinical manual segmentation results as ground truth. As expected, the results illustrate that the proposed FPDE-GLACM can provide better performance on plaque detection and segmentation. And these results may assist doctor in CAD diagnosis and treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Benjamin, E.J., Virani, S.S., Callaway, C.W., et al.: Heart disease and stroke statistics 2018 update: a report from the American Heart Association. Circulation 137, e67–e492 (2018)

    Article  Google Scholar 

  2. Kume, T., Uemura, S.: Current clinical applications of coronary optical coherence tomography. Cardiovasc. Interv. Ther. 33, 1–10 (2018)

    Article  Google Scholar 

  3. Cheimariotis, G., Chatzizisis, Y.S., Koutkias, V., et al.: ARC-OCT: automatic detection of lumen border in intravascular OCT images. Comput. Methods Progr. Biomed. 1, 21–32 (2017)

    Article  Google Scholar 

  4. Zahnd, G., Hoogendoorn, A., Combaret, N., et al.: Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions. Int. J. Comput. Assist. Radiol. Surg. 12, 1923–1936 (2017)

    Article  Google Scholar 

  5. Xu, M., Cheng, D.W.J., et al.: Graph based lumen segmentation in optical coherence tomography images. In: Proceedings of IEEE Information, Communications and Signal Processing (ICSP), pp. 1–5 (2015)

  6. Miyagawa, M., Costa, M.G.F., Gutierrez, M.A., et al.: Lumen segmentation in optical coherence tomography images using convolutional neural network. In: Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC), pp. 600–603 (2018)

  7. Xu, M., Cheng, J., Wong, D.W.K., et al.: Automatic image classification in intravascular optical coherence tomography images. In: Proceedings of IEEE Region 10 Conference (TENCON), pp. 11–22 (2016)

  8. Cao, Y., Jin, Q., Chen, Y., et al.: Automatic identification of side branch and main vascular measurements in intravascular optical coherence tomography images. In: Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), pp. 608–611 (2017)

  9. Xu, M., Cheng, J., Li, A., et al.: Fibroatheroma identification in intravascular optical coherence tomography images using deep features. In: Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1501–1504 (2017)

  10. Athanasiou, L., Karvelis, P.S., Tsakanikas, V., et al.: A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology. IEEE Trans. Inf. Technol. Biomed. 16, 391–400 (2011)

    Article  Google Scholar 

  11. Gessert, N., Lutz, M., Heyder, M., et al.: Automatic plaque detection in IV-OCT pullbacks using convolutional neural networks. IEEE Trans. Med. Imaging 38, 426–434 (2018)

    Article  Google Scholar 

  12. Athanasiou, L., Bourantas, C., Rigas, G., et al.: Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images. J. Biomed. Opt. 172, 568–580 (2014)

    Google Scholar 

  13. Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 580–587 (2014)

  14. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017)

    Article  Google Scholar 

  15. Sun, X., Wu, P., Hoi, S.C.H.: Face detection using deep learning: an improved faster R-CNN approach. Neurocomputing 299, 42–50 (2017)

    Article  Google Scholar 

  16. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 1, 266–277 (2001)

    Article  Google Scholar 

  17. Li, C., Kao, J.C.G.C., et al.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  18. Zhao, W., Xu, X., Zhu, Y., et al.: Active contour model based on local and global gaussian fitting energy for medical image segmentation. Int. J. Light Electron Opt. 158, 1160–1169 (2018)

    Article  Google Scholar 

  19. Song, T., Sanchez, V., EIDaly, H., et al.: Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images. IEEE Trans. Biomed. Eng. 64, 2913–2923 (2017)

    Article  Google Scholar 

  20. Munir, A., Soomro, S., Lee, C.H., et al.: Adaptive active contours based on variable kernel with constant initialisation. IEEE Trans. Image Process. 12, 1117–1123 (2018)

    Article  Google Scholar 

  21. Srivastava, S., Srivastava, R., Sharma, N., et al.: A fourth-order PDE-based non-linear filter for speckle reduction from optical coherence tomography images. Int. J. Biomed. Eng. Technol. 10, 59–69 (2012)

    Google Scholar 

  22. Kumar, R., Srivastava, S., Srivastava, R.: A fourth order PDE based fuzzy c-means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection. Comput. Methods Progr. Biomed. 146, 59–68 (2017)

    Article  Google Scholar 

  23. Simonyan, A.Z.K.: Very deep convolutional networks for large-scale image recognition. In: Proceeding of Learning Representations (ICLR), pp. 1–14 (2015)

  24. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.:: Going deeper with convolutions. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)

  25. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

  26. Jin, S., Su, Y., Gao, S., et al.: Deep learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms. Front. Plant Sci. 9, 866 (2018)

    Article  Google Scholar 

  27. Salah, M.B., Mitiche, A., Ayed, I.B., et al.: Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans. Image Process. 20, 545–557 (2011)

    Article  MathSciNet  Google Scholar 

  28. Fechter, T., Adebahr, S., Baltas, D., et al.: Esophagus segmentation in CT via 3D fully convolutional neural network and random walk. Med. Phys. 44, 6341–6352 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the projects of the Natural Science Foundation of Hebei Province (F2015201196), Science and Technology Research Program (QN2015135), Key Natural Science Foundation (F2017201222) and Youth Fund Projects (QN2014101) of the Hebei Province Department of Education, China.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guanglei Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Wang, G., Li, Y. et al. Faster R-CNN, fourth-order partial differential equation and global-local active contour model (FPDE-GLACM) for plaque segmentation in IV-OCT image. SIViP 14, 509–517 (2020). https://doi.org/10.1007/s11760-019-01578-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01578-2

Keywords

Navigation