Skip to main content
SpringerLink
Log in

Automatic coronary artery segmentation algorithm based on deep learning and digital image processing

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The automatic segmentation of coronary artery in coronary computed tomography angiography (CCTA) image is of great significance for clinicians to evaluate patients with coronary heart disease. When a 3D image is limited by the amount of available GPU memory, reducing the resolution of 3D image will easily lead to the loss of image detail information. Taking patches of image as input cannot make full use of image context information. Image segmentation based on deep learning is difficult to recover perfect smooth edges. The use of smooth loss function may filter out some small lesions on the coronary artery. In this paper, we present a novel CCTA image segmentation framework that combines deep learning and digital image processing algorithms to address these challenging problems. We first use V-Net to process the CCTA image with lower resolution, and get the basic feature map (rough segmentation result) with the same resolution as the original CCTA image. Then, the original CCTA image is concatenated to the basic feature map and input it into the patch-based cascaded V-shaped module to obtain a accurate coronary artery segmentation image. Finally, the center points of coronary segmentation image and the basic gradient image of the original coronary image are obtained by morphological operation. The center points of coronary artery segmentation image are used as seed points, region growing is performed on the binary basic gradient image until the white contour boundary is searched, so as to obtain a coronary segmentation result with full segmentation and smooth edges. The proposed method is analyzed quantitatively and qualitatively, and the results show that the method is better than the mainstream baseline. The ablation experiment also proved the effectiveness of each module.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Szilágyi SM, Popovici MM, Szilágyi L (2017) Automatic segmentation techniques of the coronary artery using ct images in acute coronary syndromes. J Cardiovasc Emerg 3(1):9–17

    Article  Google Scholar 

  2. Sanchis-Gomar F, Perez-Quilis C, Leischik R, Lucia A (2016) Epidemiology of coronary heart disease and acute coronary syndrome. Annals Trans Med 4(13)

  3. Wang H, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, Abraha HN, Abu-Raddad LJ, Abu-Rmeileh NM et al (2017) Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: A systematic analysis for the global burden of disease study 2016. The Lancet 390(10100):1084–1150

    Article  Google Scholar 

  4. Cury RC, Abbara S, Achenbach S, Agatston A, Berman DS, Budoff MJ, Dill KE, Jacobs JE, Maroules CD, Rubin GD et al (2016) Coronary artery disease-reporting and data system (cad-rads): An expert consensus document of scct, acr and nasci: endorsed by the acc. JACC Cardiovasc Imaging 9 (9):1099–1113

    Article  Google Scholar 

  5. Dewey M, Rutsch W, Schnapauff D, Teige F, Hamm B (2007) Coronary artery stenosis quantification using multislice computed tomography. Investig Radiol 42(2):78–84

    Article  Google Scholar 

  6. Chen YC, Lin YC, Wang CP, Lee CY, Wang TD, Lee WJ, Chen CM (2019) Coronary artery segmentation in cardiac ct angiography using 3d multi-channel u-net. In: International conference on medical imaging with deep learning–extended abstract track

  7. Kerkeni A, Benabdallah A, Manzanera A, Bedoui MH (2016) A coronary artery segmentation method based on multiscale analysis and region growing. Comput Med Imaging Graph 48:49–61

    Article  Google Scholar 

  8. Ge S, Shi Z, Peng G, Zhu Z (2019) Two-steps coronary artery segmentation algorithm based on improved level set model in combination with weighted shape-prior constraints. J Med Syst 43(7):210

    Article  Google Scholar 

  9. Cai K, Yang R, Li L, Ou S, Chen Y, Dou J (2015) A semi-automatic coronary artery segmentation framework using mechanical simulation. J Med Syst 39(10):129

    Article  Google Scholar 

  10. Zhao J, Gong W, Jiang S, Huang Z, Qin J, Tu Y, Zhou S, Ou S (2019) Automatic segmentation and reconstruction of coronary arteries based on sphere model and hessian matrix using ccta images. In: Journal of physics: Conference series, vol 1213. IOP Publishing, p 042049

  11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  12. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  13. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  14. Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging 37(7):1597–1605

    Article  Google Scholar 

  15. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE Trans Med Imaging 37(8):1822–1834

    Article  Google Scholar 

  16. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38 (10):2281–2292

    Article  Google Scholar 

  17. Ċiċek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 424–432

  18. Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3d vision (3DV). IEEE pp 565–571

  19. Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S et al (2019) nnu-net: Self-adapting framework for u-net-based medical image segmentation. In: Bildverarbeitung für die medizin 2019. Springer, pp 22–22

  20. Zhao Q, Sheng T, Wang Y, Tang Z, Chen Y, Cai L, Ling H (2019) M2det: A single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 9259–9266

  21. Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng PA (2016) 3d deeply supervised network for automatic liver segmentation from ct volumes. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 149–157

  22. Wan M, Ma L, Zhao X, Leng S, Zhang JM, San Tan R, Zhong L (2019) Automatic segmentation of coronary artery lumen via anisotropic graph-cuts. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4871–4874

  23. Jodas DS, Pereira AS, Tavares JMR (2017) Automatic segmentation of the lumen region in intravascular images of the coronary artery. Med Image Anal 40:60–79

    Article  Google Scholar 

  24. Wolterink JM, Leiner T, Išgum I (2019) Graph convolutional networks for coronary artery segmentation in cardiac ct angiography. In: International workshop on graph learning in medical imaging. Springer, pp 62–69

  25. Ulli TC, Gupta D (2020) Segmentation of calcified plaques in intravascular ultrasound images. In: Smart computing paradigms: New progresses and challenges. Springer, pp 57–67

  26. Kim S, Jang Y, Jeon B, Hong Y, Shim H, Chang H (2018) Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. In: Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis. Springer, pp 161–168

  27. Fan J, Yang J, Wang Y, Yang S, Ai D, Huang Y, Song H, Hao A, Wang Y (2018) Multichannel fully convolutional network for coronary artery segmentation in x-ray angiograms. Ieee Access 6:44635–44643

    Article  Google Scholar 

  28. Fan J, Du C, Song S, Cong W, Hao A, Yang J (2019) Enhanced subtraction image guided convolutional neural network for coronary artery segmentation. In: Chinese conference on image and graphics technologies. Springer, pp 625–632

  29. Ma G, Yang J, Huang Y, Zhao H (2019) A novel automatic coronary artery segmentation method based on region growing with annular and spherical sector partition. J Med Imaging Health Inform 9 (1):148–152

    Article  Google Scholar 

  30. Liu B, Gu L, Lu F (2019) Unsupervised ensemble strategy for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 111–119

  31. Ma G, Yang J, Zhao H (2020) A coronary artery segmentation method based on region growing with variable sector search area. Technol Health Care (Preprint) 1–10

  32. Ma G, Yang J, Huang Y, Zhao H (2019) A novel automatic coronary artery segmentation method based on region growing with annular and spherical sector partition. J Med Imaging Health Inform 9 (1):148–152

    Article  Google Scholar 

  33. Ansari MA, Zai S, Moon YS (2017) Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding. Opt Eng 56(1):013106

    Article  Google Scholar 

  34. Fu Y, Guo BJ, Lei Y, Wang T, Liu T, Curran W, Zhang LJ, Yang X (2020) Mask r-cnn based coronary artery segmentation in coronary computed tomography angiography. In: Medical imaging 2020: Computer-aided diagnosis. International society for optics and photonics, vol 11314, p 113144f

  35. Kong B, Wang X, Bai J, Lu Y, Gao F, Cao K, Xia J, Song Q, Yin Y (2020) Learning tree-structured representation for 3d coronary artery segmentation. Comput Med Imaging Graph 80:101688

    Article  Google Scholar 

  36. Blaiech AG, Mansour A, Kerkeni A, Bedoui MH, Abdallah AB (2019) Impact of enhancement for coronary artery segmentation based on deep learning neural network. In: Iberian conference on pattern recognition and image analysis. Springer, pp 260–272

  37. Wang L, Liang D, Yin X, Qiu J, Yang Z, Xing J, Dong J, Ma Z (2020) Coronary artery segmentation in angiographic videos utilizing spatial-temporal information. BMC Med Imaging 20(1):1–10

    Article  Google Scholar 

  38. Yu F, Zhao J, Gong Y, Wang Z, Li Y, Yang F, Dong B, Li Q, Zhang L (2019) Annotation-free cardiac vessel segmentation via knowledge transfer from retinal images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 714–722

  39. Shen Y, Fang Z, Gao Y, Xiong N, Zhong C, Tang X (2019) Coronary arteries segmentation based on 3d fcn with attention gate and level set function. IEEE Access 7:42826–42835

    Article  Google Scholar 

  40. Zhai M, Du T, Yang R, Zhang H (2019) Coronary artery vascular segmentation on limited data via pseudo-precise label. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 816–819

  41. Xiao C, Li Y, Jiang Y (2020) Heart coronary artery segmentation and disease risk warning based on a deep learning algorithm. IEEE Access 8:140108–140121

    Article  Google Scholar 

  42. Cui H, Xia Y, Zhang Y (2020) Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images. Int J Numer Methods Biomed Eng e3348

  43. Lei Y, Guo B, Fu Y, Wang T, Liu T, Curran W, Zhang L, Yang X (2020) Automated coronary artery segmentation in coronary computed tomography angiography (ccta) using deep learning neural networks. In: Medical imaging 2020: Imaging informatics for healthcare, research, and applications. International society for optics and photonics, vol 11318, p 1131812

  44. Zhang S, Fu H, Yan Y, Zhang Y, Wu Q, Yang M, Tan M, Xu Y (2019) Attention guided network for retinal image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 797–805

  45. Huang C, Han H, Yao Q, Zhu S, Zhou SK (2019) 3d u2-net: A 3d universal u-net for multi-domain medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 291–299

Download references

Author information

Authors and Affiliations

  1. Shanghai University Of Engineering Science, School of Electronic and Electrical Engineering, 333 Longteng Road, Shanghai, 201620, China

    Fangzheng Tian, Yongbin Gao, Zhijun Fang & Jia Gu

Authors
  1. Fangzheng Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongbin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhijun Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongbin Gao.

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

Tian, F., Gao, Y., Fang, Z. et al. Automatic coronary artery segmentation algorithm based on deep learning and digital image processing. Appl Intell 51, 8881–8895 (2021). https://doi.org/10.1007/s10489-021-02197-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02197-6

Keywords

Search

Navigation