Skip to main content
SpringerLink
Log in

Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. From 2013 to 2019, DCE-CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average three-dimensional Dice’s similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were mainly employed to evaluate the segmentation accuracy, based on a nested fourfold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71 mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84 mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.

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

Similar content being viewed by others

References

  1. Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Villanueva A (2019) Hepatocellular carcinoma. N Engl J Med 380:1450–1462. https://doi.org/10.1056/nejmra1713263

    Article  CAS  PubMed  Google Scholar 

  3. World Health Organization Liver Factsheet. Globocan. https://gco.iarc.fr/today/data/factsheets/cancers/11-Liver-fact-sheet.pdf. Accessed Jun 11, 2022.

  4. Kim E, Viatour P (2020) Hepatocellular carcinoma: old friends and new tricks. Exp Mol Med 52:1898–1907. https://doi.org/10.1038/s12276-020-00527-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yamamoto M, Yoshida M, Furuse J, Sano K, Ohtsuka M, Yamashita S et al (2021) Clinical practice guidelines for the management of liver metastases from extrahepatic primary cancers 2021. J Hepato-Biliary-Pancreatic Sci 28(1):1–25

    Article  CAS  Google Scholar 

  6. The Japan society of hepatology (2017) Clinical practice guidelines for hepatocellular carcinoma 2017. Kanehara & Co. LTD, Tokyo

    Google Scholar 

  7. Tarhan NC, Hatipoğlu T, Ercan E, Bener M, Keleş G, Başaran C et al (2011) Correlation of dynamic multidetector CT findings with pathological grades of hepatocellular carcinoma. Diagn Interv Radiol 17(4):328–333. https://doi.org/10.4261/1305-3825.dir.2682-09.3

    Article  PubMed  Google Scholar 

  8. Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR et al (2021) Radiomics analysis of contrast-enhanced CT for hepatocellular carcinoma grading. Front Oncol 11:660509. https://doi.org/10.3389/fonc.2021.660509

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pérez-Saborido B, de los Galanes SJ, Menéu-Dı́az JC, Romero CJ, Elola-Olaso AM, Suárez YF et al (2007) Tumor recurrence after liver transplantation for hepatocellular carcinoma: recurrence pathway and prognostic factors. Transplant Proc 39(7):2304–2307. https://doi.org/10.1016/j.transproceed.2007.06.059

    Article  PubMed  Google Scholar 

  10. Shinkawa H, Tanaka S, Kabata D, Takemura S, Amano R, Kimura K et al (2021) The prognostic impact of tumor differentiation on recurrence and survival after resection of hepatocellular carcinoma is dependent on tumor size. Liver cancer 10(5):461–472. https://doi.org/10.1159/000517992

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kim YS, Kim JW, Yoon WS, Kang MK, Lee IK, Kim TH et al (2016) Interobserver variability in gross tumor volume delineation for hepatocellular carcinoma. Strahlenther Onkol 192:714–721. https://doi.org/10.1007/s00066-016-1028-2

    Article  PubMed  Google Scholar 

  12. Yasaka K, Akai H, Abe O, Kiryu S (2018) Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. Radiology 286:887–896. https://doi.org/10.1148/radiol.2017170706

    Article  PubMed  Google Scholar 

  13. Yuan Y (2017) Hierarchical convolutional-deconvolutional neural networks for automatic liver and tumor segmentation. arXiv preprint arXiv:1710.04540.

  14. Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA (2018) H-Dense UNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674

    Article  PubMed  Google Scholar 

  15. Qayyum A, Lalande A, Meriaudeau F (2020) Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging. Comput Biol Med 127:104097. https://doi.org/10.1016/j.compbiomed.2020.104097

    Article  PubMed  Google Scholar 

  16. Alirr OI (2020) Deep learning and level set approach for liver and tumor segmentation from CT scans. J Appl Clin Med Phys 21(10):200–209. https://doi.org/10.1002/acm2.13003

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bilic P, Christ P F, Vorontsov E, Chlebus G, Chen H, Dou Q et al (2019) The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056.

  18. Zhang W, Yang R, Liang F, Liu G, Chen A, Wu H et al (2021) Prediction of microvascular invasion in hepatocellular carcinoma with a multi-disciplinary team-like radiomics fusion model on dynamic contrast-enhanced computed tomography. Front Oncol 11:1–11. https://doi.org/10.3389/fonc.2021.660629

    Article  Google Scholar 

  19. Cui Y, Arimura H, Nakano R, Yoshitake T, Shioyama Y, Yabuuchi H (2021) Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks. J Radiat Res 62(2):346–355. https://doi.org/10.1093/jrr/rraa132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  21. Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. IEEE/CVF conference on computer vision and pattern recognition (CVPR) Published online 2019. https://doi.org/10.1109/cvpr.2019.00075

  22. Chen X, Sun S, Bai N, Han K, Liu Q, Yao S et al (2021) A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol 160:175–184. https://doi.org/10.1016/j.radonc.2021.04.019

    Article  PubMed  Google Scholar 

  23. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. https://doi.org/10.1186/s12880-015-0068-x

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dora L, Agrawal S, Panda R, Abraham A (2018) Nested cross-validation based adaptive sparse representation algorithm and its application to pathological brain classification. Expert Syst Appl 114:313–321. https://doi.org/10.1016/j.eswa.2018.07.039

    Article  Google Scholar 

  25. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174

    Article  CAS  PubMed  Google Scholar 

  26. 3D Slicer. https://www.slicer.org/

  27. Smith DJ (2018) A nearest-neighbour discretisation of the regularized stokeslet boundary integral equation. J Comput Phys 358:88–102. https://doi.org/10.1016/j.jcp.2017.12.008

    Article  CAS  Google Scholar 

  28. Yamada A, Oyama K, Fujita S, Yoshizawa E, Ichinohe F, Komatsu D et al (2019) Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary? Int J Comput Assist Radiol Surg 14(8):1295–1301. https://doi.org/10.1007/s11548-019-01987-1

    Article  PubMed  Google Scholar 

  29. Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. International conference on 3D vision. IEEE, Stanford, pp 565–571

    Google Scholar 

  30. Tieleman T, Hinton G (2012) Lecture 6.5-RMSProp: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw Mach Learn 4(2):26–31

    Google Scholar 

  31. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A (2021) A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65:545–563. https://doi.org/10.1111/1754-9485.13261

    Article  PubMed  Google Scholar 

  32. Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G et al (2018) NiffyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rodarmel C, Shan J (2002) Principal component analysis for hyperspectral image classification. Surv L Inf Sci 62(2):115–122

    Google Scholar 

  34. Liu Z, Liu F, Chen W, Liu X, Hou X, Shen J et al (2021) Automatic segmentation of clinical target volumes for post-modified radical mastectomy radiotherapy using convolutional neural networks. Front Oncol 10:581347. https://doi.org/10.3389/fonc.2020.581347

    Article  PubMed  PubMed Central  Google Scholar 

  35. R. https://www.r-project.org/

  36. Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA et al (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). Int Symp Biomed Imagin. https://doi.org/10.1109/ISBI.2018.8363547

    Article  Google Scholar 

  37. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128:336–359

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially supported by a grant from Center for Clinical and Translational Research of Kyushu University Hospital and JSPS KAKENHI Grant Number JP20K08084. The authors are grateful to all members of the Arimura Laboratory (http://web.shs.kyushu-u.ac.jp/~arimura/ c.jp/~arimura/), Department of radiology, Saga University Hospital for their valuable comments and helpful discussion and to Hirokazu Takahashi, Satoshi Oeda and Hiroshi Isoda, Liver center, Saga University Hospital for their comments for clinical practice of HCC.

Author information

Authors and Affiliations

  1. Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Noriyuki Nagami, Cui Yunhao & Kenta Ninomiya

  2. Department of Radiology, Saga University Hospital, 5-1-1, Nabeshima, Saga City, Saga, 849-8501, Japan

    Noriyuki Nagami, Manabu Ogata & Shigetoshi Kitamura

  3. Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Hidetaka Arimura

  4. Medical Corporation Kouhoukai, Takagi Hospital, 141-11, Sakemi, Okawa City, Fukuoka, 831-0016, Japan

    Junichi Nojiri

  5. Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City , Saga, 849-8501, Japan

    Junichi Nojiri, Mitsutoshi Oishi, Keiichi Ohira & Hiroyuki Irie

Authors
  1. Noriyuki Nagami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hidetaka Arimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junichi Nojiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cui Yunhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kenta Ninomiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manabu Ogata
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mitsutoshi Oishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Keiichi Ohira
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shigetoshi Kitamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hiroyuki Irie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: HA, NN, Data curation: HA, NN, MO, JN, Formal analysis: NN, MO, KO, Funding acquisition: HA, Investigation: NN, HA, Methodology: NN, HA, Project administration: HA, Resources: NN, HA, JN, CY, KN, MO, SK, HI, Software: NN, KN, CY, Supervision: HA, SK, HI, Validation: NN, HA, Visualization: NN, HA, Writing—original draft: NN, HA.

Corresponding author

Correspondence to Hidetaka Arimura.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Nagami, N., Arimura, H., Nojiri, J. et al. Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images. Phys Eng Sci Med 46, 83–97 (2023). https://doi.org/10.1007/s13246-022-01202-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-022-01202-7

Keywords

Search

Navigation