Empirical Comparisons of Deep Learning Networks on Liver Segmentation |
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Authors: | Yi Shen Victor S Sheng Lei Wang Jie Duan Xuefeng Xi Dengyong Zhang Ziming Cui |
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Affiliation: | 1.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
2 Department of Computer Science, University of Central Arkansas, Conway, Arkansas, USA.
3 Huan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer
and Communication Engineering, Changsha University of Science and Technology, Changsha, China. |
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Abstract: | Accurate segmentation of CT images of liver tumors is an important adjunct
for the liver diagnosis and treatment of liver diseases. In recent years, due to the great
improvement of hard device, many deep learning based methods have been proposed for
automatic liver segmentation. Among them, there are the plain neural network headed by
FCN and the residual neural network headed by Resnet, both of which have many
variations. They have achieved certain achievements in medical image segmentation. In
this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet,
Resnet and Densenet, to investigate their performance on liver segmentation. Since
original Resnet and Densenet could not perform image segmentation directly, we make
some adjustments for them to perform live segmentation. Our experimental results show
that Densenet performs the best on liver segmentation, followed by Resnet. Both perform
much better than Segnet, U-Net, and FCN. Among Segnet, U-Net, and FCN, U-Net
performs the best, followed by Segnet. FCN performs the worst. |
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Keywords: | Liver segmentation deep learning FCN U-Net Segnet Resnet Densenet |
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