@Article{cmc.2020.010069,
AUTHOR = {Yong Luo, Xiaojie Li, Chao Luo, Feng Wang, Xi Wu, Imran Mumtaz, Cheng Yi},
TITLE = {Tissue Segmentation in Nasopharyngeal CT Images Using TwoStage Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1771--1780},
URL = {http://www.techscience.com/cmc/v65n2/39905},
ISSN = {1546-2226},
ABSTRACT = {Tissue segmentation is a fundamental and important task in nasopharyngeal 
images analysis. However, it is a challenging task to accurately and quickly segment 
various tissues in the nasopharynx region due to the small difference in gray value 
between tissues in the nasopharyngeal image and the complexity of the tissue structure. 
In this paper, we propose a novel tissue segmentation approach based on a two-stage 
learning framework and U-Net. In the proposed methodology, the network consists of 
two segmentation modules. The first module performs rough segmentation and the 
second module performs accurate segmentation. Considering the training time and the 
limitation of computing resources, the structure of the second module is simpler and the 
number of network layers is less. In addition, our segmentation module is based on U-Net 
and incorporates a skip structure, which can make full use of the original features of the 
data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal 
dataset provided by West China Hospital of Sichuan University. The experimental results 
show that the proposed method is superior to many standard segmentation structures and 
the recently proposed nasopharyngeal tissue segmentation method, and can be easily 
generalized across different tissue types in various organs.},
DOI = {10.32604/cmc.2020.010069}
}