Segmenting Hepatocellular Carcinoma in Multi-phase CT

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)


Liver cancer diagnosis and treatment response assessment typically rely on the inspection of multi-phase contrast-enhanced computed tomography (CT) or magnetic resonance (MR) images. To date, various methods were proposed to automatically segment liver lesions in single time-step CT; but limited research addressed image analysis of multiple contrast phases. In this paper, we propose a multi-encoder 3D U-Net which, inspired by radiological practice, combines complementary tumour characteristics from both the arterial phase (AP) and portal venous phase (PVP) CT images. We demonstrate that encoder-decoder networks with disentangled feature extraction in two encoder streams outperform the baseline U-Nets that process single-phase data alone or apply input-level fusion for stacks of multi-phase data as channel input. Finally, we make use of a public single-phase CT liver tumour dataset for the pre-training of network parameters to improve the generalisation capabilities of our networks.


Hepatocellular carcinoma Multi-phase CT segmentation Cascaded U-Net Feature fusion 



This research is funded by the H2020 MSCA-ITN PREDICT project and supported by Perspectum Ltd. The authors would also like to thank the University College London, and in particular Tim Meyer, for providing the TACE 2 trial dataset and the UK Royal Academy of Engineering for its support under its Engineering for Development Research fellowship scheme.


  1. 1.
    Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Eisenhauer, E.A., et al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45(2), 228–247 (2009)CrossRefGoogle Scholar
  4. 4.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  5. 5.
    Stawiaski, J., Decenciere, E., Bidault, F.: Interactive liver tumor segmentation using graph-cuts and watershed. In: 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2008) (2008)Google Scholar
  6. 6.
    Moltz, J.H., Bornemann, L., Dicken, V., Peitgen, H.: Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. In: MICCAI workshop, vol. 41, p. 195 (2008)Google Scholar
  7. 7.
    Shimizu, A., Narihira, T., Furukawa, D., Kobatake, H., Nawano, S., Shinozaki, K.: Ensemble segmentation using adaboost with application to liver lesion extraction from a CT volume. In: Proceedings of MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge II., NY, USA (2008)Google Scholar
  8. 8.
    Vorontsov, E., Abi-Jaoudeh, N., Kadoury, S.: Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models. In: Yoshida, H., Näppi, J., Saini, S. (eds.) Abdominal Imaging Computational and Clinical Applications, ABD-MICCAI 2014. Lecture Notes in Computer Science, vol. 8676, pp. 74–83. Springer, Cham (2014). Scholar
  9. 9.
    Christ, P.F., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415–423. Springer, Cham (2016). Scholar
  10. 10.
    Bilic, P., et al.: The liver tumor segmentation benchmark (LITS) (2019). arXiv preprint arXiv:1901.04056
  11. 11.
    Chlebus, G., Schenk, A., Moltz, J.H., van Ginneken, B., Hahn, H.K., Meine, H.: Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci. Rep. 8(1), 15497 (2018)CrossRefGoogle Scholar
  12. 12.
    Han, X.: Automatic liver lesion segmentation using a deep convolutional neural network method (2017). arXiv preprint arXiv:1704.07239
  13. 13.
    Wang, X., Han, S., Chen, Y., Gao, D., Vasconcelos, N.: Volumetric attention for 3D medical image segmentation and detection. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 175–184. Springer, Cham (2019). Scholar
  14. 14.
    Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-based medical image segmentation (2018). arXiv preprint arXiv:1809.10486
  15. 15.
    Isensee, F., Petersen, J., Kohl, S.A.A., Jäger, P.F., Maier-Hein, K.H.: nnU-Net: breaking the spell on successful medical image segmentation (2019). CoRR, vol. abs/1904.08128Google Scholar
  16. 16.
    Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)CrossRefGoogle Scholar
  17. 17.
    Sun, C., et al.: Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif. Intell. Med. 83, 58–66 (2017)CrossRefGoogle Scholar
  18. 18.
    Wu, Y., Zhou, Q., Hu, H., Rong, G., Li, Y., Wang, S.: Hepatic lesion segmentation by combining plain and contrast-enhanced CT images with modality weighted U-Net. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 255–259. IEEE (2019)Google Scholar
  19. 19.
    Meyer, T., et al.: Sorafenib in combination with transarterial chemoembolisation in patients with unresectable hepatocellular carcinoma (TACE 2): a randomised placebo-controlled, double-blind, phase 3 trial. Lancet Gastroenterol. Hepatol. 2(8), 565–575 (2017)CrossRefGoogle Scholar
  20. 20.
    Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., Heng, P.-A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imag. 37(12), 2663–2674 (2018)CrossRefGoogle Scholar
  21. 21.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
  22. 22.
    Chen, H., et al.: MMFNet: a multi-modality MRI fusion network for segmentation of nasopharyngeal carcinoma. Neurocomputing 394, 27–40 (2020)CrossRefGoogle Scholar
  23. 23.
    Dolz, J., Ben Ayed, I., Desrosiers, C.: Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 271–282. Springer, Cham (2019). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Perspectum Ltd.OxfordUK

Personalised recommendations