Abstract

Info

Title:: Automatic liver tumor segmentation based on cascaded 3D convolutional neural network

Author(s):: LI Yuanqiang1; WU Yuli1; YANG Xiaoping2; 1. School of Science, Nanjing University of Science and Technology, Nanjing 210094, China; 2. Department of Mathematics, Nanjing University, Nanjing 210094, China

Keywords:: liver tumor; automatic segmentation; cascaded convolutional neural network; residual structure

Abstract:: Objective To propose a cascade convolutional neural network for full-automatic liver tumor segmentation in CT image according to the specificity of liver tumor CT image, difficulty of liver tumor segmentation and the idea of residual network. Methods Firstly, CT data were preprocessed based on clinical information, thus reducing interferences. Then a coarse liver segmentation network was used for liver segmentation, and according to the location of segmentation results, the liver was selected as the region of interest. Finally, the tumor was segmented accurately in the region of interest. Results A fast segmentation of liver tumor was realized by cascaded network segmentation which effectively reduced computational time and avoided interferences from other tissues. The proposed method was tested on the dataset of MICCAI 2017 liver tumor segmentation challenge (LiTS) and achieved an average Dice score of 0.663, which verified its effectiveness in the segmentation of liver tumor. Conclusion The full-automatic liver tumor segmentation in CT image based on cascaded convolutional neural network can be used to realize fast tumor segmentation. More cases will be included and tumor classification will be conducted in later studies, so as to further improve the model.