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The Influence of Different Segmentation Methods on the Extraction of Imaging Histological Features of Hepatocellular Carcinoma CT

  • Sen Zhao
  • Wenyan Ren
  • Yan Zhuang
  • Zhixue WangEmail author
Image & Signal Processing
  • 18 Downloads
Part of the following topical collections:
  1. Artificial Intelligence Application in Health Informatics

Abstract

In order to analyze the influence of different segmentation techniques on hepatocellular carcinoma (HCC) CT (Computed Tomography) imaging histological feature extraction, Grow Cut method and Graph Cut method are used to segment hepatocellular carcinoma from arterial CT images of HCC patients, and the stability and repeatability of imaging histological features are studied. Meanwhile, hierarchical clustering method is used to reduce the redundancy of features. The results show that the repeatability and redundancy mainly depend on the method of tumor segmentation. Semi-automatic segmentation method can improve the repeatability of image features, and hierarchical clustering can reduce the redundancy of features. Different segmentation techniques have different effects on the extraction of histological features of CT images of HCC.

Keywords

Segmentation HCC Imaging histological features Repeatability 

Notes

Compliance with ethical standards

Conflict of interest

Author Sen Zhao declares that he has no conflict of interest. Author Wenyan Ren declares that he has no conflict of interest. Author Yan Zhuang declares that he has no conflict of interest. Author Zhixue Wang declares that he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Clark, T., Maximin, S., Meier, J., Pokharel, S., and Bhargava, P., Hepatocellular carcinoma: Review of epidemiology, screening, imaging diagnosis, response assessment, and treatment. Curr. Probl. Diagn. Radiol. 44(6):479–486, 2015.CrossRefGoogle Scholar
  2. 2.
    Chen, C. Y., Chen, J., Xia, C. C., Huang, Z. X., and Song, B., T1 mapping combined with Gd-EOB-DTPA-enhanced magnetic resonance imaging in predicting the pathologic grading of hepatocellular carcinoma. J. Biol. Regul. Homeost. Agents 31(4):1029–1036, 2017.PubMedGoogle Scholar
  3. 3.
    Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., and Bray, F., Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5):359–386, 2015.CrossRefGoogle Scholar
  4. 4.
    Scholz, M., Konen, W., Tombrock, S., Fricke, B., Adams, L., von Düring, M., Hentsch, A., Heuser, L., and Harders, A. G., Development of an endoscopic navigation system based on digital image processing. Computer Aided Surgery 3(3):134–143, 2015.CrossRefGoogle Scholar
  5. 5.
    Sayed, G. I., Hassanien, A. E., and Schaefer, G., An automated computer-aided diagnosis system for abdominal CT liver images. Procedia Computer Science 90:68–73, 2016.CrossRefGoogle Scholar
  6. 6.
    Kadoury, S., Vorontsov, E., and Tang, A., Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys. Med. Biol. 60(16):6459–6478, 2015.CrossRefGoogle Scholar
  7. 7.
    Lee, D. H., and Lee, J., Primary malignant tumours in the non-cirrhotic liver. Eur. J. Radiol. 95:349, 2017.CrossRefGoogle Scholar
  8. 8.
    Petré, C., Ni, Y., Marcha, G., Yu, J., Wevers, M., Lauffer, R. B., and Baert, A. L., Detection and characterization of primary liver cancer in rats by MS-264-enhanced MRI. Magnetic Resonance in Medicine Official Journal of the Society of Magnetic Resonance in Medicine 35(4):532–539, 2015.CrossRefGoogle Scholar
  9. 9.
    Choi, J. Y., Lee, J. M., and Sirlin, C. B., CT and MR imaging diagnosis and staging of hepatocellular carcinoma: Part II. Extracellular agents, hepatobiliary agents, and ancillary imaging features[J]. Radiology 273(1):30–50, 2014.CrossRefGoogle Scholar
  10. 10.
    Fujita, N., Asayama, Y., Nishie, A., Ishigami, K., Ushijima, Y., Takayama, Y., Okamoto, D., Moirta, K., Shirabe, K., Aishima, S., Wang, H., Oda, Y., and Honda, H., Mass-forming intrahepatic cholangiocarcinoma: Enhancement patterns in the arterial phase of dynamic hepatic CT - correlation with clinicopathological findings. Eur. Radiol. 27(2):1–9, 2016.Google Scholar
  11. 11.
    Brown, K. T., Do, R. K., Gonen, M. et al., Randomized trial of hepatic artery embolization for hepatocellular carcinoma using doxorubicin-eluting microspheres compared with embolization with microspheres alone[J]. J. Clin. Oncol. 34(17):2046, 2016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Medical Imaging CenterThe First Affiliated Hospital of Henan UniversityKaifeng CityChina

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