Research on automatic segmentation of tumor target of lung cancer in CBCT images by multimodal style transfer technology based on deep learning
10.3760/cma.j.cn113030-20201103-00531
- VernacularTitle:基于深度学习多模态风格迁移技术CBCT图像肺癌靶区自动分割方法研究
- Author:
Jie CHEN
1
;
Keqiang WANG
;
Jianbo JIAN
;
Peng WANG
;
Zhichao GUO
;
Wenxue ZHANG
Author Information
1. 天津医科大学总医院放疗科，天津 300052
- Keywords:
Deep learning;
Style transformation;
Target automatic segmentation;
Lung cancer
- From:
Chinese Journal of Radiation Oncology
2022;31(1):43-48
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Due to the low contrast between tumors and surrounding tissues in CBCT images, this study was designed to propose an automatic segmentation method for central lung cancer in CBCT images.Methods:There are 221 patients with central lung cancer were recruited. Among them, 176 patients underwent CT localization and 45 patients underwent enhanced CT localization. The enhanced CT images were set as the lung window and mediastinal window, and elastic registration was performed with the first CBCT validation images to obtain the paired data set. The CBCT images could be transformed into" enhanced CT" under the lung window and mediastinal window by loading the paired data sets into cycleGAN network for style transformation. Finally, the transformed images were loaded into the UNET-attention network for deep learning of GTV. The results of segmentation were evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD) and the area under the receiver operating characteristic curve (AUC).Results:The contrast between tumors and surrounding tissues was significantly improved after style transformation. The DSC value of cycleGAN+ UNET-attention network was 0.78±0.05, HD value was 9.22±3.42 and AUC value was 0.864, respectively.Conclusion:The cycleGAN+ UNET-attention network can effectively segment central lung cancer in CBCT images.
