Next Article in Journal
ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting
Previous Article in Journal
Heuristic Method for Minimizing Model Size of CNN by Combining Multiple Pruning Techniques
Previous Article in Special Issue
CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation
 
 
Article

Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images

by 1,2,†, 1,2,3,† and 1,2,*
1
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
2
Sun Yat-sen University, Guangzhou 510275, China
3
School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Loris Nanni
Sensors 2022, 22(15), 5875; https://doi.org/10.3390/s22155875
Received: 30 May 2022 / Revised: 23 July 2022 / Accepted: 30 July 2022 / Published: 5 August 2022
Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models. View Full-Text
Keywords: lightweight; nasopharyngeal carcinoma; deep learning; medical image segmentation lightweight; nasopharyngeal carcinoma; deep learning; medical image segmentation
Show Figures

Figure 1

MDPI and ACS Style

Liu, Y.; Han, G.; Liu, X. Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images. Sensors 2022, 22, 5875. https://doi.org/10.3390/s22155875

AMA Style

Liu Y, Han G, Liu X. Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images. Sensors. 2022; 22(15):5875. https://doi.org/10.3390/s22155875

Chicago/Turabian Style

Liu, Yi, Guanghui Han, and Xiujian Liu. 2022. "Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images" Sensors 22, no. 15: 5875. https://doi.org/10.3390/s22155875

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop