Skip to main content

Advertisement

SpringerLink
Log in

A novel method to model hepatic vascular network using vessel segmentation, thinning, and completion

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

Abstract

The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient’s abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination, which can obtain a complete liver vascular network. First, the CT image was preprocessed by grayscale mapping based on sigmoid function, vessel enhancement based on Hessian filter, and denoising based on anisotropic filter to enhance the grayscale contrast between the vascular and non-vascular parts of the liver. Then, the liver vessels were initially segmented based on the improved three-dimensional graph cut algorithm. Based on the obtained liver vascular structure, the vessel centerline of the liver was then extracted by the proposed thinning algorithm that continuously traversed the foreground voxel points and iteratively deleted the simple points. Finally, the combination of vascular centerline optimization was used to predict and link the vascular centerline fractured portion. The under-segmented liver vessels were complemented based on the complete vascular centerline tree. To verify the proposed hepatic vascular segmentation and complementation algorithm, the open 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb) was applied to test and quantify the results. The results showed that the proposed algorithm can accurately and effectively segment the vascular network structure from abdominal CT images, and the proposed vascular complementation method can restore the true information of under-segmented liver vessels.

A novel hepatic vessel segmentation method from abdominal CT images was proposed, including graph cut algorithm, centerline extraction, and broken vessel completion. First, the graph cut algorithm was used to obtain the initial segmentation result. Then, the centerline of the initial segmentation result was extracted. Finally, the initial segmentation result was optimized through centerline analysis.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J (2016) Cancer statistics in China, 2015. CA: a cancer journal for clinicians 66(2):115–132. https://doi.org/10.3322/caac.21338

    Article  Google Scholar 

  2. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA: a cancer journal for clinicians 65(2):87–108. https://doi.org/10.3322/caac.21262

    Article  Google Scholar 

  3. Wang H, Naghavi M, Allen C et al (2016) Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388(10053):1459–1544. https://doi.org/10.1016/S0140-6736(16)31012-1

    Article  Google Scholar 

  4. Marcan M, Pavliha D, Music M M, , Fuckan I., Magjarevic R., Miklavcic D.et al. (2014) Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver. Radiol Oncol 48(3): 267–-281. https://doi.org/10.2478/raon-2014-0022

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wang Y, Fang B, Pi J et al (2013) Automatic multi-scale segmentation of intrahepatic vessel in CT images for liver surgery planning. Int J Pattern Recognit Artif Intell 27(01):1357001. https://doi.org/10.1142/S0218001413570012

    Article  Google Scholar 

  6. Foruzan AH, Chen YW, Zoroofi RA et al (2012) Analysis of CT images of liver for surgical planning. American Journal of Biomedical Engineering 2012 2(2):23–28. https://doi.org/10.5923/j.ajbe.20120202.05

    Article  Google Scholar 

  7. Kaftan JN, Tek H, Aach T (2009) A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images. Medical imaging 2009: image processing. https://doi.org/10.1117/12.812407

  8. Bauer C, Pock T, Sorantin E, , Bischof H., Beichel R.et al. (2010) Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts. Med Image Anal 14(2): 172–-184. https://doi.org/10.1016/j.media.2009.11.003

    Article  PubMed  Google Scholar 

  9. Pamulapati V, Wood BJ, Linguraru MG (2011) Intra-hepatic vessel segmentation and classification in multi-phase CT using optimized graph cuts. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011:1982–1985. https://doi.org/10.1109/ISBI.2011.5872799

    Article  Google Scholar 

  10. Chen B, Sun Y, Ong SH (2014) Liver vessel segmentation using graph cuts with quick shift initialization. The 15th International Conference on Biomedical Engineering. https://doi.org/10.1007/978-3-319-02913-9_48

  11. Zeng Y, Zhao Y, Tang P, , Liao M., Liang Y.X., Liao S.H., Zou B.J. et al. (2017) Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. Comput Methods Prog Biomed (150): 31–-39. https://doi.org/10.1016/j.cmpb.2017.07.002

    Article  Google Scholar 

  12. Chung M, Lee J, Chung J W, , Shin Y.G. et al. (2018) Accurate liver vessel segmentation via active contour model with dense vessel candidates. Comput Methods Prog Biomed (166): 61–-75. https://doi.org/10.1016/j.cmpb.2018.10.010

    Article  Google Scholar 

  13. Yang X, Do Yang J, Hwang HP et al (2018) Segmentation of liver and vessels from CT images and classification of liver segments for preoperative liver surgical planning in living donor liver transplantation. Comput Methods Prog Biomed 158:41–52. https://doi.org/10.1016/j.cmpb.2017.12.008

    Article  Google Scholar 

  14. Goceri E, Shah ZK, Gurcan MN (2017) Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach. International journal for numerical methods in biomedical engineering 33(4):e2811. https://doi.org/10.1002/cnm.2811

    Article  Google Scholar 

  15. Kitrungrotsakul T, Han XH, Iwamoto Y et al (2019) Vessel Net: A Deep Convolutional Neural Network with Multi Pathways for Robust Hepatic Vessel Segmentation. Computerized Medical Imaging and Graphics

  16. Thomson B R, Nijkamp J, Ivashchenko O, et al. (2019) Hepatic vessel segmentation using a reduced filter 3D U-Net in ultrasound imaging.

  17. Huang Q, Sun J, Ding H (2018) Robust liver vessel extraction using 3D U-Net with variant dice loss function. Comput Biol Med 101:153–162. https://doi.org/10.1016/j.compbiomed.2018.08.018

    Article  PubMed  Google Scholar 

  18. Ibragimov B, Toesca D, Chang D, , Koong A., Xing L.et al. (2017) Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol 62(23): 8943. https://doi.org/10.1088/1361-6560/aa9262, 8958

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang R, Zhou Z, Wu W (2018) An improved fuzzy connectedness method for automatic three-dimensional liver vessel segmentation in CT images. J Healthcare Eng. https://doi.org/10.1155/2018/2376317

  20. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. International Conference on Medical Image Computing and Computer-Assisted Intervention:130–137. https://doi.org/10.1007/BFb0056195

  21. Zhai Z, Staring M, Stoel BC (2016) Lung vessel segmentation in CT images using graph cuts. Medical imaging 2016: image processing. International Society for Optics and Photonics 9784. https://doi.org/10.1117/12.2216827

  22. Palágyi K, Balogh E, Kuba A, Halmai C, Erdőhelyi B, Sorantin E, Hausegger K (2001) A sequential 3D thinning algorithm and its medical applications, Biennial International Conference on Information Processing in Medical Imaging:409–415. https://doi.org/10.1007/3-540-45729-1_42

  23. Soler L, Hostettler A, Agnus V, Charnoz A, Fasquel J, Moreau J, Osswald A, Bouhadjar M, Marescaux J (2010) 3D image reconstruction for comparison of algorithm database: A patient specific anatomical and medical image database,.

  24. Luu HM, Klink C, Moelker A (2015) Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images. Phys Med Biol 60(10):3905–3926. https://doi.org/10.1088/0031-9155/60/10/3905

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported in part by grants from the National Key Research and Development Program of China (2017YFB1002804, 2017YFB1401203), the Major program of National Social Science Foundation of China (17ZDA331), National Natural Science Foundation of China (61701022), Beijing Natural Science Foundation (7182158), Beijing Science & Technology Program (Z181100001018017),and the Fundamental Research Funds for the Central Universities (FRF-TP-19-015A2).

Author information

Author notes

  1. Xiaoyu Guo and Ruoxiu Xiao contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Xiaoyu Guo, Ruoxiu Xiao, Cheng Chen, Jiayu Wang & Zhiliang Wang

  2. Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, 100853, China

    Tao Zhang

Authors
  1. Xiaoyu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruoxiu Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiayu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhiliang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruoxiu Xiao.

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

Guo, X., Xiao, R., Zhang, T. et al. A novel method to model hepatic vascular network using vessel segmentation, thinning, and completion. Med Biol Eng Comput 58, 709–724 (2020). https://doi.org/10.1007/s11517-020-02128-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02128-6

Keywords

Search

Navigation