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Segmenting Hepatocellular Carcinoma in Multi-phase CT

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

Abstract

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.

Keywords

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

Notes

Acknowledgment

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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