Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection
Accurate localization of lung tumor in real time based on a single X-ray projection is of great interest to the tumor-tracking radiotherapy but is very challenging. In this paper, a convolutional neural network (CNN)-based tumor localization method was proposed to address this problem with the aid o...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8642382/ |
| _version_ | 1818914444108890112 |
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| author | Ran Wei Fugen Zhou Bo Liu Xiangzhi Bai Dongshan Fu Yongbao Li Bin Liang Qiuwen Wu |
| author_facet | Ran Wei Fugen Zhou Bo Liu Xiangzhi Bai Dongshan Fu Yongbao Li Bin Liang Qiuwen Wu |
| author_sort | Ran Wei |
| collection | DOAJ |
| description | Accurate localization of lung tumor in real time based on a single X-ray projection is of great interest to the tumor-tracking radiotherapy but is very challenging. In this paper, a convolutional neural network (CNN)-based tumor localization method was proposed to address this problem with the aid of principal component analysis-based motion modeling. A CNN regression model was trained before treatment to recover the ill-conditioned nonlinear mapping from the single X-ray projection to the tumor motion. Novel intensity correction and data augmentation techniques were adopted to improve the model's robustness to the scatter and noise in the X-ray projection image. During treatment, the volumetric image and tumor position could be obtained by applying the CNN model on the acquired X-ray projection. This method was validated and compared with the other state-of-the-art methods on three real patient data. It was found that the proposed method could achieve real-time tumor localization with much higher accuracy (<;1 mm) and robustness. |
| first_indexed | 2024-12-19T23:46:29Z |
| format | Article |
| id | doaj.art-3e9b75aad63f4281a2e9b014e7c6fb2f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:46:29Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3e9b75aad63f4281a2e9b014e7c6fb2f2022-12-21T20:01:17ZengIEEEIEEE Access2169-35362019-01-017370263703810.1109/ACCESS.2019.28993858642382Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray ProjectionRan Wei0https://orcid.org/0000-0003-4274-7447Fugen Zhou1Bo Liu2Xiangzhi Bai3https://orcid.org/0000-0002-6115-8237Dongshan Fu4Yongbao Li5Bin Liang6Qiuwen Wu7Image Processing Center, Beihang University, Beijing, ChinaImage Processing Center, Beihang University, Beijing, ChinaImage Processing Center, Beihang University, Beijing, ChinaImage Processing Center, Beihang University, Beijing, ChinaBeijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, ChinaDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, USAAccurate localization of lung tumor in real time based on a single X-ray projection is of great interest to the tumor-tracking radiotherapy but is very challenging. In this paper, a convolutional neural network (CNN)-based tumor localization method was proposed to address this problem with the aid of principal component analysis-based motion modeling. A CNN regression model was trained before treatment to recover the ill-conditioned nonlinear mapping from the single X-ray projection to the tumor motion. Novel intensity correction and data augmentation techniques were adopted to improve the model's robustness to the scatter and noise in the X-ray projection image. During treatment, the volumetric image and tumor position could be obtained by applying the CNN model on the acquired X-ray projection. This method was validated and compared with the other state-of-the-art methods on three real patient data. It was found that the proposed method could achieve real-time tumor localization with much higher accuracy (<;1 mm) and robustness.https://ieeexplore.ieee.org/document/8642382/Convolutional neural network (CNN)PCA breathing motion modelingsingle x-ray projectiontumor localizationvolumetric imaging |
| spellingShingle | Ran Wei Fugen Zhou Bo Liu Xiangzhi Bai Dongshan Fu Yongbao Li Bin Liang Qiuwen Wu Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection IEEE Access Convolutional neural network (CNN) PCA breathing motion modeling single x-ray projection tumor localization volumetric imaging |
| title | Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection |
| title_full | Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection |
| title_fullStr | Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection |
| title_full_unstemmed | Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection |
| title_short | Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection |
| title_sort | convolutional neural network cnn based three dimensional tumor localization using single x ray projection |
| topic | Convolutional neural network (CNN) PCA breathing motion modeling single x-ray projection tumor localization volumetric imaging |
| url | https://ieeexplore.ieee.org/document/8642382/ |
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