@Article{cmc.2020.07450,
AUTHOR = {Yi Shen, Victor S. Sheng, Lei Wang, Jie Duan, Xuefeng Xi, Dengyong Zhang, Ziming Cui},
TITLE = {Empirical Comparisons of Deep Learning Networks on Liver Segmentation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1233--1247},
URL = {http://www.techscience.com/cmc/v62n3/38351},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07450}
}