Application of U-Net network in automatic image segmentation of adenoid and airway of nasopharynx.
10.13201/j.issn.2096-7993.2023.08.006
- Author:
Lu WANG
1
;
Zebin LUO
2
;
Jianhui NI
1
;
Yan LI
1
;
Liqing CHEN
1
;
Shuwen GUAN
1
;
Nannan ZHANG
1
;
Xin WANG
1
;
Rong CAI
1
;
Yi GAO
2
;
Qingfeng ZHANG
1
Author Information
1. Department of Otorhinolaryngology Head and Neck Surgery，Shenzhen University General Hospital，Shenzhen University Clinical Medical Academy，Shenzhen University，Shenzhen，518055，China.
2. School of Biomedical Engineering，Health Science Center，Shenzhen University.
- Publication Type:Journal Article
- Keywords:
U-Net network;
adenoid;
airway of nasopharynx;
cone beam computed tomography;
fully automatic image segmentation
- MeSH:
Child;
Humans;
Adolescent;
Adenoids/diagnostic imaging*;
Image Processing, Computer-Assisted/methods*;
Pharynx;
Cone-Beam Computed Tomography;
Nose
- From:
Journal of Clinical Otorhinolaryngology Head and Neck Surgery
2023;37(8):632-641
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the effect of fully automatic image segmentation of adenoid and nasopharyngeal airway by deep learning model based on U-Net network. Methods:From March 2021 to March 2022, 240 children underwent cone beam computed tomography（CBCT） in the Department of Otolaryngology, Head and Neck Surgery, General Hospital of Shenzhen University. 52 of them were selected for manual labeling of nasopharynx airway and adenoid, and then were trained and verified by the deep learning model. After applying the model to the remaining data, compare the differences between conventional two-dimensional indicators and deep learning three-dimensional indicators in 240 datasets. Results:For the 52 cases of modeling and training data sets, there was no significant difference between the prediction results of deep learning and the manual labeling results of doctors（P>0.05）. The model evaluation index of nasopharyngeal airway volume: Mean Intersection over Union（MIOU） s （86.32±0.54）%; Dice Similarity Coefficient（DSC）: （92.91±0.23）%; Accuracy: （95.92±0.25）%; Precision: （91.93±0.14）%; and the model evaluation index of Adenoid volume: MIOU: （86.28±0.61）%; DSC: （92.88±0.17）%; Accuracy: （95.90±0.29）%; Precision: （92.30±0.23）%. There was a positive correlation between the two-dimensional index A/N and the three-dimensional index AV/（AV+NAV） in 240 children of different age groups（P<0.05）, and the correlation coefficient of 9-13 years old was 0.74. Conclusion:The deep learning model based on U-Net network has a good effect on the automatic image segmentation of adenoid and nasopharynx airway, and has high application value. The model has a certain generalization ability.
