Abstract
Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods.
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Acknowledgements
This work is partially supported by National Natural Science Foundation of China (61972187, 61772254), Fujian Provincial Leading Project (2017H0030, 2019H0025), Government Guiding Regional Science and Technology Development (2019L3009), and Natural Science Foundation of Fujian Province (2017J01768 and 2019J01756).
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Jin, L., Ma, R., Zhao, M., Teng, S., Li, Z. (2020). Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_15
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