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.
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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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Vogt, N., Brady, S.M., Ridgway, G., Connell, J., Namburete, A.I.L. (2020). Segmenting Hepatocellular Carcinoma in Multi-phase CT. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_7
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