Skip to main content
SpringerLink
Log in

3D vessel extraction using a scale-adaptive hybrid parametric tracker

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.

Graphical abstract

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms – review of methods, datasets and evaluation metric. Comput Meth Prog Biomed 158:71–79. https://doi.org/10.1016/j.cmpb.2018.02.001

    Article  Google Scholar 

  2. Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3d vessel lumen segmentation techniques: Models, features and extraction schemes. Med Image Anal 13:819–845. https://doi.org/10.1016/j.media.2009.07.011

    Article  PubMed  Google Scholar 

  3. Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R (1998) Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 2:143–168. https://doi.org/10.1016/S1361-8415(98)80009-1

    Article  CAS  PubMed  Google Scholar 

  4. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Proc Int Conf Med Image Comput-Assist Intervent, pp 130–137

  5. Olabarriaga SD, Breeuwer M, Niessen WJ (2003) Evaluation of hessian-based filters to enhance the axis of coronary arteries in ct images. In: Proc 17th Int Congr Exhib, pp 1191–1196. https://doi.org/10.1016/S0531-5131(03)00307-8

  6. Orowski P, Orkisz M (2009) Efficient computation of hessian-based enhancement filters for tubular structures in 3d images. Innovation and Research in BioMed Eng 30:128–132. https://doi.org/10.1016/j.irbm.2009.04.003

    Article  Google Scholar 

  7. Manniesing R, Velthuis BK, van Leeuwen MS, van der Schaaf IC, van Laar PJ, Niessen WJ (2006) Level set based cerebral vasculature segmentation and diameter quantification in ct angiography. Med Image Anal 10:200–214. https://doi.org/10.1016/j.media.2005.09.001

  8. Krissian K (2002) Flux-based anisotropic diffusion applied to enhancement of 3-d angiograms. IEEE Trans Med Imag 21:1440–1442. https://doi.org/10.1109/TMI.2002.806403

    Article  Google Scholar 

  9. Law MWK, Chung ACS (2008) Three dimensional curvilinear structure detection using optimally oriented flux. In: Proc Eur Conf Comput Vision, pp 368–382

  10. Lesage D, Angelini ED, Bloch I, FunkaLea G (2009) Design and study of flux-based features for 3d vascular tracking. In: Proc IEEE Int Symp Biomed Imaging, p 286–289. https://doi.org/10.1109/ISBI.2009.5193040

  11. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vision 1:321–331. https://doi.org/10.1007/BF00133570

    Article  Google Scholar 

  12. Brigger P, Hoeg J, Unser M (2000) B-spline snakes: a flexible tool for parametric contour detection. Trans Image Process 9:1484–1496. https://doi.org/10.1109/83.862624

    Article  CAS  Google Scholar 

  13. Nain D, Yezzi AJ, Turk G (2004) Vessel segmentation using a shape driven flow. In: Proc Int Conf Med Image Comput-Assist Intervent, pp 51–59

  14. Cruz AL, Straka M, Kochl A, Sramek M, Fleischmann D (2004) Nonlinear model fitting to parameterize diseased blood vessels. In: Proc IEEE Visulization, pp 393–400. https://doi.org/10.1109/VISUAL.2004.72

  15. Lee SH, Lee S (2015) Adaptive kalman snake for semi-autonomous 3d vessel tracking. Comput Methods Programs Biomed 122:56–75. https://doi.org/10.1016/j.cmpb.2015.06.008

    Article  PubMed  Google Scholar 

  16. Wong WCK, Chung ACS (2017) Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths. Med Image Anal 11:567–587. https://doi.org/10.1016/j.media.2007.05.003

    Article  Google Scholar 

  17. Wörz S, Rohr K (2007) Segmentation and quantification of human vessels using a 3-d cylindrical intensity model. IEEE Trans Image Process 16:1994–2004. https://doi.org/10.1109/TIP.2007.901204

    Article  PubMed  Google Scholar 

  18. Tyrrell JA, Tomaso Ed, D F, Tong R, Kozak K, Jain RK, Roysam B (2007) Robust 3-d modeling of vasculature imagery using superellipsoids. IEEE Trans Med Imag 26:223–237. https://doi.org/10.1109/TMI.2006.889722

  19. Lee J, Beighley P, Ritman E, Smith N (2007) Automatic segmentation of 3d micro-ct coronary vascular images. Med Image Anal 11:630–647. https://doi.org/10.1016/j.media.2007.06.012

    Article  PubMed  Google Scholar 

  20. Schaap M, Smal I, Metz C, van Walsum T, Niessen W (2007) Bayesian tracking of elongated structures in 3d images. In: Proc Inf Process Med Imaging, pp 74–85

  21. Friman O, Hindennach M, Kühnel C, Peitgen HO (2010) Multiple hypothesis template tracking of small 3d vessel structures. Med Image Anal 14:160–171. https://doi.org/10.1016/j.media.2009.12.003

    Article  PubMed  Google Scholar 

  22. Xu C, Pham D, Prince J (2000) Image segmentation using deformable models. Handbool of Mecial Imaging 2:129–174. https://doi.org/10.1117/3.831079.ch3

    Article  Google Scholar 

  23. Li H, Yezzi AJ (2007) Vessels as 4-d curves: Global minimal 4-d paths to extract 3-d tubular surfaces and centerlines. IEEE Trans Med Imag 26:1213–1223. https://doi.org/10.1109/tmi.2007.903696

    Article  Google Scholar 

  24. Benmansour F, Cohen LD (2009) Tubular anisotropy for 3d vessels segmentation. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2286–2293. https://doi.org/10.1109/CVPR.2009.5206703

  25. Litjens G, Kooi T, Bejnordi B, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez C (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  PubMed  Google Scholar 

  26. Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: Proc 4th Int Conf 3D Vis, pp 565–571

  27. Çiçek O, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: Proc Int Conf Med Image Comput-Assist Intervent, p 424–432

  28. Yan Z, Yang X, Cheng KT (2016) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65:1912–1923. https://doi.org/10.1109/TBME.2018.2828137

    Article  Google Scholar 

  29. Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A (2016) Deep mri brain extraction: A 3d convolutional neural network for skull stripping. Neuroimage 129:460–469. https://doi.org/10.1016/j.neuroimage.2016.01.024

    Article  PubMed  Google Scholar 

  30. Charbonnier JP, van Rikxoort EM, Setio AAA, Schaefer-Prokop CM, van Ginneken B, Ciompi F (2017) Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal 36:52–60. https://doi.org/10.1016/j.media.2016.11.001

    Article  PubMed  Google Scholar 

  31. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans Med Imag 35:1240–1251. https://doi.org/10.1109/TMI.2016.2538465

    Article  Google Scholar 

  32. Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61–78

    Article  PubMed  Google Scholar 

  33. Oda M, Roth HR, Kitasaka T, Misawa K, Fujiwara M, Mori K (2019) Abdominal artery segmentation method from ct volumes using fully convolutional neural network. Int J Comput Assist Radiol Surg 14:2069–2081. https://doi.org/10.1007/s11548-019-02062-5

    Article  PubMed  Google Scholar 

  34. Wolterink JM, van Hamersvelt RW, Viergever MA, Leiner T, Išgum I (2019) Coronary artery centerline extraction in cardiac ct angiography using a cnn-based orientation classifier. Med Image Anal 51:46–60. https://doi.org/10.1016/j.media.2018.10.005

    Article  PubMed  Google Scholar 

  35. Fu F, Wei J, Zhang M, Yu F, Xiao Y, Rong D, Shan Y, Li Y, Zhao C, Liao F, Yang Z, Li Y, Chen Y, Wang X, Lu J (2020) Rapid vessel segmentation and reconstruction of head and neck angiograms using 3d convolutional neural network. Nat Commun 11. https://doi.org/10.1038/s41467-020-18606-2

  36. He JP, Y C, Can Z MW, Z Y, Y X, Yizhou (2020) Learning hybrid representations for automatic 3d vessel centerline extraction. In: Proc Int Conf Med Image Comput-Assist Intervent, pp 24–34

  37. Bruno P, Zaffino P, Scaramuzzino S, De Rosa S, Indolfi C, Calimeri F, Spadea M, Ghidini C, Magnini B, Passerini A, Traverso P (2018) Using cnns for designing and implementing an automatic vascular segmentation method of biomedical images. In: AI*IA 2018 – Advances in Artificial Intelligence, pp 60–70. https://doi.org/10.1007/978-3-030-03840-3_5

  38. Bruno P, Spadea M, Scaramuzzino S, De Rosa S, Indolfi C, Gargiulo G, Giugliano G, Esposito G, Calimeri F, Zaffino P (2022) Assessing vascular complexity of paod patients by deep learning-based segmentation and fractal dimension. Neural Comput Applic 34:22015–22022. https://doi.org/10.1007/s00521-022-07642-2

    Article  Google Scholar 

  39. Florin C, Paragios N, Williams J (2005) A quasi-monte carlo solution for segmentation of coronaries. In: Proc Int Conf Med Image Comput-Assist Intervent, pp 246–253

  40. Krissian K, Wu X, Luboz V (2006) Smooth vasculature reconstruction with circular and elliptic cross sections. In: Proc Int Medicine Meets Virtual Reality Conference

  41. Shim H, Kwon D, Yun I, Lee S (2006) Robust segmentation of cerebral arterial segments by a sequential monte carlo method: particle filtering. Comput Methods and Programs in Biomedicine 84:135–145

    Article  Google Scholar 

  42. La Cruz A, Straka M, Köchl A, Srámek E M nad Gröller, Fleischmann D (2005) Nonlinear model fitting to parameterize diseased blood vessels. In: Proc Int Conf IEEE Visualization, pp 393–400

  43. Gill P, Murray W (1978) Algorithms for the solution of the nonlinear least-squares problem. SIAM Journal on Numerical Analysis 15:977–992

    Article  Google Scholar 

  44. Egger J, Tokuda J, Chauvin L, Freisleben B, Nimsky C, Kapur T, Wells W (2012) Integration of the openigtlink network protocol for image-guided therapy with the medical platform mevislab. Int J Med Robot 8:282–290

    Article  PubMed  PubMed Central  Google Scholar 

  45. Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proc IEEE Int Conf Comput Vis, pp 105–112

  46. Zheng Y, Tek H, Funka-Lea (2013) Robust and accurate coronary artery centerline extraction in cta by combining model-driven and data-driven approaches. In: Proc Int Conf Med Image Comput-Assist Intervent, pp 74–81

  47. Schaap M, Metz CT, van Walsum T, van der Giessen AG, Weustink AC, Mollet NR et al (2009) Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 13:701–714

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 61971118).

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China

    Qi Sun, Jinzhu Yang, Shuang Ma, Yan Huang & Yuliang Yuan

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Qi Sun, Jinzhu Yang, Shuang Ma, Yan Huang & Yuliang Yuan

  3. Department of Radiology, ShengJing Hospital of China Medical University, Shenyang, Liaoning, China

    Yang Hou

Authors
  1. Qi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinzhu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuliang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinzhu Yang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Q., Yang, J., Ma, S. et al. 3D vessel extraction using a scale-adaptive hybrid parametric tracker. Med Biol Eng Comput 61, 2467–2480 (2023). https://doi.org/10.1007/s11517-023-02815-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02815-0

Keywords

Search

Navigation