Empirical Comparisons of Deep Learning Networks on Liver Segmentation
Yi Shen1, Victor S. Sheng1, 2, *, Lei Wang1, Jie Duan1, Xuefeng Xi1, Dengyong Zhang3, Ziming Cui1
CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1233-1247, 2020, DOI:10.32604/cmc.2020.07450
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 More >