Skip to main content
SpringerLink
Log in

Two new stenosis detection methods of coronary angiograms

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Coronary angiography is the “gold standard” for diagnosing coronary artery disease. At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice.

Methods

Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this method, the user can choose any vessel segment to do further analysis of its stenoses.

Results

Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and \(F_1\) score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality.

Conclusion

The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availibility Statement

The used data are available upon request from Qing Zhang.

Code Availability Statement

Private code repository.

References

  1. Husmann L, Leschka S, Desbiolles L, Schepis T, Gaemperli O, Seifert B, Cattin P, Frauenfelder T, Flohr TG, Marincek B, Kaufmann PA, Alkadhi H (2007) Coronary Artery Motion and Cardiac Phases: Dependency on Heart Rate-Implications for CT Image Reconstruction. Radiology 245(2):567–576

    Article  Google Scholar 

  2. Roth GA, Abate D, Abate K, Hassen A, Solomon M, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A et al (2018) Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017. Lancet 392(10159):1736–1788

    Article  Google Scholar 

  3. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN et al (2020) Heart disease and stroke statistics-2020 update: a report from the american heart association. Circulation 141(9):e139–e596

    Article  Google Scholar 

  4. Song J, Zheng Z, Wang W, Song Y, Huang J, Wang H, Zhao H, Hu S (2013) Assessment of Coronary Artery Stenosis by Coronary Angiography: A Head-to-Head Comparison With Pathological Coronary Artery Anatomy. Circ Cardiovasc Interv 6(3):262–268

  5. Makowski P, Sørensen TS, Therkildsen SV, Materka A, Stødkilde-Jørgensen H, Pedersen EM (2002) Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from mri images for 3d visualization. Comput Med Imaging Graph 26(1):9–17

    Article  Google Scholar 

  6. Carrillo JF, Hoyos MH, Dávila EE, Orkisz M (2007) Recursive tracking of vascular tree axes in 3d medical images. Int J Comput Assist Radiol Surg 1(6):331–339

    Article  Google Scholar 

  7. Manniesing R, Viergever MA, Niessen WJ (2007) Vessel axis tracking using topology constrained surface evolution. IEEE Trans Med Imaging 26(3):309–316

    Article  Google Scholar 

  8. Xiao R, Yang J, Goyal M, Liu Y, Wang Y (2013) Automatic Vasculature Identification in Coronary Angiograms by Adaptive Geometrical Tracking. Comput Math Methods Med 1–11:2013

    Google Scholar 

  9. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp S (eds) Medical image computing and computer-assisted intervention–MICCAI’98. Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, pp 130–137

    Google Scholar 

  10. Cruz-Aceves I, Cervantes-Sanchez F, Avila-Garcia Maria S (2018) A novel multiscale Gaussian-matched filter using neural networks for the segmentation of x-ray coronary angiograms. J Healthc Eng

  11. Jo K, Kweon J, Kim Y-H, Choi J (2018) Segmentation of the main vessel of the left anterior descending artery using selective feature mapping in coronary angiography. IEEE Access 7:919–930

    Article  Google Scholar 

  12. Fang H, Zhu J, Ai D, Huang Y, Jiang Y, Song H, Wang Y, Yang J (2019) Greedy soft matching for vascular tracking of coronary angiographic image sequences. IEEE Trans Circuits Syst Video Technol 30(5):1466–1480

    Article  Google Scholar 

  13. Xian Z, Wang X, Yan S, Yang D, Chen J, Peng C (2020) Main coronary vessel segmentation using deep learning in smart medical. Math Probl Eng

  14. Nasr-Esfahani E, Karimi N, Jafari MH, Soroushmehr SMR, Samavi S, Nallamothu BK, Najarian K (2018) Segmentation of vessels in angiograms using convolutional neural networks. Biomed Signal Process Control 40:240–251

    Article  Google Scholar 

  15. Danilov VV, Klyshnikov KY, Gerget OM, Kutikhin AG, Ganyukov VI, Frangi AF, Ovcharenko EA (2021) Real-time coronary artery stenosis detection based on modern neural networks. Sci Rep 11(1):7582

    Article  CAS  Google Scholar 

  16. Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) Deepvessel: retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 132–139

  17. Yan Z, Yang X, Cheng K-T (2018) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform 23(4):1427–1436

    Article  Google Scholar 

  18. Oliveira A, Pereira S, Silva CA (2018) Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst Appl 112:229–242

    Article  Google Scholar 

  19. Jin Q, Meng Z, Pham TD, Chen Q, Wei L, Su R (2019) Dunet: a deformable network for retinal vessel segmentation. Knowl-Based Syst 178:149–162

    Article  Google Scholar 

  20. Wu Y, Xia Y, Song Y, Zhang Y, Cai W (2020) Nfn+: a novel network followed network for retinal vessel segmentation. Neural Netw 126:153–162

    Article  Google Scholar 

  21. Wang D, Haytham A, Pottenburgh J, Saeedi O, Tao Y (2020) Hard attention net for automatic retinal vessel segmentation. IEEE J Biomed Health Inform 24(12):3384–3396

    Article  Google Scholar 

  22. Chen C, Chuah JH, Raza A, Wang Y (2021) Retinal vessel segmentation using deep learning: a review. IEEE Access

  23. Cong C, Kato Y, Vasconcellos HD, Lima J, Venkatesh B (2019) Automated stenosis detection and classification in x-ray angiography using deep neural network. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1301–1308

  24. Ovalle-Magallanes E, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J (2020) Transfer learning for stenosis detection in x-ray coronary angiography. Mathematics 8(9):1510

    Article  Google Scholar 

  25. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1–4):259–268

    Article  Google Scholar 

  26. Malin DF (1977) Unsharp masking. AAS Photo Bull 16:10–13

    Google Scholar 

  27. Ericksen JP, Yankaskas BC, Muller KE, Pizer SM, Johnston RE (1990) Contrast-limited adaptive histogram equalization: speed and effectiveness. In: [1990] Proceedings of the first conference on visualization in biomedical computing, pp 337–345

  28. Khan MA, Chen W, Ullah A, Fu Z (2017) A mesh-free algorithm for ROF model. EURASIP J Adv Signal Process 2017(1):53

    Article  Google Scholar 

  29. Deng G (2017) A generalized unsharp masking algorithm. IEEE Trans Image Process 20(5):1249–1261

    Article  Google Scholar 

  30. Setiawan AW, Mengko TR, Santoso OS, Suksmono AB (2013) Color retinal image enhancement using CLAHE. In: International conference on ICT for smart society, pp 1–3

  31. Vese LA, Chan TF (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  32. Tian Y, Chen Q, Wang W, Peng Y, Wang Q, Duan F, Wu Z, Zhou M (2014) A vessel active contour model for vascular segmentation. BioMed Res Int 2014:106490

    PubMed  PubMed Central  Google Scholar 

  33. Neubauer AM, Garcia JA, Messenger JC, Hansis E, Kim MS, Klein AJP, Schoonenberg GAF, Grass M, Carroll JD (2010) Clinical feasibility of a fully automated 3d reconstruction of rotational coronary x-ray angiograms. Circul Cardiovasc Interv 3(1):71–79

    Article  Google Scholar 

Download references

Funding

Yaofang Liu, Xinyue Zhang, Wenlong Wan, Shaoyu Liu, Yingdi Liu, Hu Liu, and Xueying Zeng were supported by the National Natural Science Foundation of China [Nos. 11771408, 11871444] and the Fundamental Research Funds for the Central Universities [No. 201964006]. Qing Zhang was supported by the National Natural Science Foundation of China [No. 81671703], the Key Fund of Department of Cardiology, Shandong University Qilu Hospital (Qingdao) [QDKY2019ZD04], the Key Research and Development Project of Shandong Province [No. 2015GSF118026], the Qingdao Key Health Discipline Development Fund, and People’s Livelihood Science and Technology Project of Qingdao [No. 18-6-1-62-nsh].

Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China

    Yaofang Liu, Xinyue Zhang, Yingdi Liu & Xueying Zeng

  2. School of Computer Science and Technology, Ocean University of China, Qingdao, Shandong, China

    Wenlong Wan & Shaoyu Liu

  3. School of Material Science and Engineering, Ocean University of China, Qingdao, Shandong, China

    Hu Liu

  4. Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China

    Qing Zhang

Authors
  1. Yaofang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinyue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenlong Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingdi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xueying Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yaofang Liu, Xinyue Zhang, and Wenlong Wan contributed equally.

Ethics declarations

Conflict of interest

Authors do not have any conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Not applicable.

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

Liu, Y., Zhang, X., Wan, W. et al. Two new stenosis detection methods of coronary angiograms. Int J CARS 17, 521–530 (2022). https://doi.org/10.1007/s11548-021-02551-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02551-6

Keywords

Search

Navigation