Segmenting Hepatocellular Carcinoma in Multi-phase CT

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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