Open Access
ARTICLE
Tissue Segmentation in Nasopharyngeal CT Images Using TwoStage Learning
Yong Luo1, Xiaojie Li2, Chao Luo2, Feng Wang1, Xi Wu2, Imran Mumtaz3, Cheng Yi1, *
1 West China Hospital, Sichuan University, Chengdu, 610000, China.
2 Chengdu University of Information Technology, Chengdu, 610000, China.
3 University of Agriculture, Faisalabad, 38000, Pakistan.
* Corresponding Author: Cheng Yi. Email: .
Computers, Materials & Continua 2020, 65(2), 1771-1780. https://doi.org/10.32604/cmc.2020.010069
Received 10 February 2020; Accepted 22 April 2020; Issue published 20 August 2020
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.
Keywords
Cite This Article
Y. Luo, X. Li, C. Luo, F. Wang, X. Wu
et al., "Tissue segmentation in nasopharyngeal ct images using twostage learning,"
Computers, Materials & Continua, vol. 65, no.2, pp. 1771–1780, 2020.
Citations