Abstract
Liver cancer is a common malignant tumor and one of the main causes of cancer deaths in the world. Accurately segmenting liver tumor regions from the liver has a very important guiding role for doctors in disease diagnosis and surgical planning. Thus, the method of automated segmentation of liver tumors has important value for clinical diagnosis and treatment. This paper proposes a method that combines fully convolutional neural networks based on two-dimensional convolution and those based on three-dimensional convolution. The results show that the liver tumor segmentation effect of this method is much better than that of only using single two-dimensional convolutional neural networks or three-dimensional convolutional neural networks.
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Project Source: 2019 Basic scientific research operating cost project of provincial undergraduate universities in Heilongjiang Province, Project number: 2019-KYYWF-1252.
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Mu, W. et al. (2021). Study on Liver Tumor Segmentation Technology Based on Fully Convolutional Networks. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_102
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DOI: https://doi.org/10.1007/978-3-030-70042-3_102
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Online ISBN: 978-3-030-70042-3
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