Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy
Accurately determining tumor regions from stereo fluoroscopic images during radiotherapy is a challenging task. As a result, high-density fiducial markers are implanted around tumors in clinical practice as internal surrogates of the tumor, which leads to associated surgical risks. This study was co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10471547/ |
| _version_ | 1827308241092083712 |
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| author | Yongxuan Yan Fumitake Fujii Takehiro Shiinoki Shengping Liu |
| author_facet | Yongxuan Yan Fumitake Fujii Takehiro Shiinoki Shengping Liu |
| author_sort | Yongxuan Yan |
| collection | DOAJ |
| description | Accurately determining tumor regions from stereo fluoroscopic images during radiotherapy is a challenging task. As a result, high-density fiducial markers are implanted around tumors in clinical practice as internal surrogates of the tumor, which leads to associated surgical risks. This study was conducted to achieve lung tumor localization without the use of fiducial markers. We propose training a cascade U-net system to perform color to grayscale conversion, enhancement, bone suppression, and tumor detection to determine the precise tumor region. We generated Digitally Reconstructed Radiographs (DRRs) and tumor labels from 4D planning CT images as training data. An improved maximum projection algorithm and a novel color-to-gray conversion algorithm were proposed to improve the quality of the generated training data. Training a bone suppression model using bone-enhanced and bone-suppressed DRRs enables the bone suppression model to achieve better bone suppression performance. The mean peak signal-to-noise ratios in the test sets of the trained translation and bone suppression models are 39.284 ± 0.034 dB and 37.713 ± 0.724 dB, respectively. The results indicate that our proposed markerless tumor localization method is applicable in seven out of ten cases; in applicable cases, the centroid position error of the tumor detection model is less than 1.13 mm; and the calculated tumor center motion trajectories using the proposed network highly coincide with the motion trajectories of implanted fiducial markers in over 60% of captured groups, providing a promising direction for markerless tumor localization tracking methods. |
| first_indexed | 2024-04-24T18:55:01Z |
| format | Article |
| id | doaj.art-56d674aa30dc4f70806e5567e3b9bbd8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:55:01Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56d674aa30dc4f70806e5567e3b9bbd82024-03-26T17:44:07ZengIEEEIEEE Access2169-35362024-01-0112408094082610.1109/ACCESS.2024.337674410471547Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for RadiotherapyYongxuan Yan0https://orcid.org/0000-0002-3069-2867Fumitake Fujii1https://orcid.org/0000-0002-6496-5755Takehiro Shiinoki2https://orcid.org/0000-0002-0246-8489Shengping Liu3https://orcid.org/0000-0002-0603-9972Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, JapanGraduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, JapanGraduate School of Medicine, Yamaguchi University, Ube, JapanSchool of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, ChinaAccurately determining tumor regions from stereo fluoroscopic images during radiotherapy is a challenging task. As a result, high-density fiducial markers are implanted around tumors in clinical practice as internal surrogates of the tumor, which leads to associated surgical risks. This study was conducted to achieve lung tumor localization without the use of fiducial markers. We propose training a cascade U-net system to perform color to grayscale conversion, enhancement, bone suppression, and tumor detection to determine the precise tumor region. We generated Digitally Reconstructed Radiographs (DRRs) and tumor labels from 4D planning CT images as training data. An improved maximum projection algorithm and a novel color-to-gray conversion algorithm were proposed to improve the quality of the generated training data. Training a bone suppression model using bone-enhanced and bone-suppressed DRRs enables the bone suppression model to achieve better bone suppression performance. The mean peak signal-to-noise ratios in the test sets of the trained translation and bone suppression models are 39.284 ± 0.034 dB and 37.713 ± 0.724 dB, respectively. The results indicate that our proposed markerless tumor localization method is applicable in seven out of ten cases; in applicable cases, the centroid position error of the tumor detection model is less than 1.13 mm; and the calculated tumor center motion trajectories using the proposed network highly coincide with the motion trajectories of implanted fiducial markers in over 60% of captured groups, providing a promising direction for markerless tumor localization tracking methods.https://ieeexplore.ieee.org/document/10471547/Fluoroscopic imageimage-guided radiotherapymarkerless tumor localizationmotion managementcascaded U-nets |
| spellingShingle | Yongxuan Yan Fumitake Fujii Takehiro Shiinoki Shengping Liu Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy IEEE Access Fluoroscopic image image-guided radiotherapy markerless tumor localization motion management cascaded U-nets |
| title | Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy |
| title_full | Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy |
| title_fullStr | Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy |
| title_full_unstemmed | Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy |
| title_short | Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy |
| title_sort | markerless lung tumor localization from intraoperative stereo color fluoroscopic images for radiotherapy |
| topic | Fluoroscopic image image-guided radiotherapy markerless tumor localization motion management cascaded U-nets |
| url | https://ieeexplore.ieee.org/document/10471547/ |
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