Effects of metal artifacts on automatic segmentation of organs-at-risk in patients receiving radiotherapy for nasopharyngeal carcinoma(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第10期
- Page:
- 1185-1189
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Effects of metal artifacts on automatic segmentation of organs-at-risk in patients receiving radiotherapy for nasopharyngeal carcinoma
- Author(s):
- SONG Wei; LU Hong; MA Jun; ZHAO Di; WANG Yijun; HUANG Wei; QIN Liang; YU Dahai
- Department of Radiation Oncology, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China
- Keywords:
- Keywords: nasopharyngeal carcinoma organs-at-risk automatic segmentation deep learning metal artifacts
- PACS:
- R318；R815.6
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.10.001
- Abstract:
- Abstract: Objective To evaluate the effects of CT metal artifacts on the automatic segmentation of organs-at-risk (OAR) in nasopharyngeal carcinoma (NPC) patients treated with radiotherapy. Methods Two groups of NPC patients with or without dental restorations were enrolled, with 16 patients in each group, and a total of 26 kinds of OAR were segmented by an experienced radiation oncologist and a deep learning-based automatic segmentation platform (AccuContour), separately. The three-dimensional Dice similarity coefficient (DSC) and Hausdorff distance (HD) of different OAR in patients with or without metal artifacts were compared, and moreover, the two-dimensional DSC and HD of mandibles and oral cavity on axial slices with or without metal artifacts were also compared. Meanwhile, the time taken for manual and automatic segmentations was recorded. Results There were no significant differences in three-dimensional DSC and HD for all OAR between patients with metal artifacts and those without metal artifacts (P>0.05). The two-dimensional DSC and HD of the oral cavity on axial slices without metal artifacts were better than those on axial slices with metal artifacts (P<0.01). The more severe the artifact was, the greater the deviation of automatically segmented contours of oral cavity from the ground truth was. The efficiency of automatic segmentation was significantly higher than manual segmentation (<2 min vs >70 min). Conclusion Dental restoration artifacts has limited effects on the accuracy and efficiency of the deep learning-based automatic segmentation of OAR in radiotherapy for NPC, and automatic segmentation still has distinct advantages over manual segmentation.
Last Update: 2021-10-28