Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network.
10.12122/j.issn.1673-4254.2020.11.07
- Author:
Xin YANG
1
,
2
,
3
,
4
;
Xueyan LI
1
,
2
,
3
,
4
;
Xiaoting ZHANG
1
,
2
,
3
,
4
;
Fan SONG
1
,
2
,
3
,
4
;
Sijuan HUANG
1
,
2
,
3
,
4
;
Yunfei XIA
1
,
2
,
3
,
4
Author Information
1. Sun Yat- sen University Cancer Center
2. State Key Laboratory of Oncology in South China
3. Collaborative Innovation Center for Cancer Medicine
4. Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.
- Publication Type:Journal Article
- Keywords:
AUnet;
CT images;
auto segmentation;
deep learning;
improved Unet architecture
- MeSH:
Databases, Factual;
Humans;
Image Processing, Computer-Assisted;
Nasopharyngeal Carcinoma/radiotherapy*;
Nasopharyngeal Neoplasms/radiotherapy*;
Organs at Risk;
Tomography, X-Ray Computed
- From:
Journal of Southern Medical University
2020;40(11):1579-1586
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To investigate the accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma (NPC).
METHODS:The CT image data of 147 NPC patients with manual segmentation of the OARs were randomized into the training set (115 cases), validation set (12 cases), and the test set (20 cases). An improved network based on three-dimensional (3D) Unet was established (named as AUnet) and its efficiency was improved through end-to-end training. Organ size was introduced as a priori knowledge to improve the performance of the model in convolution kernel size design, which enabled the network to better extract the features of different organs of different sizes. The adaptive histogram equalization algorithm was used to preprocess the input CT images to facilitate contour recognition. The similarity evaluation indexes, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated to verify the validity of segmentation.
RESULTS:DSC and HD of the test dataset were 0.86±0.02 and 4.0±2.0 mm, respectively. No significant difference was found between the results of AUnet and manual segmentation of the OARs (
CONCLUSIONS:AUnet, an improved deep learning neural network, is capable of automatic segmentation of the OARs in radiotherapy for NPC based on CT images, and for most organs, the results are comparable to those of manual segmentation.
