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Abstract
Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (Dv) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in Dv between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification.
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Details
1 Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Department of Radiation Therapy, Hangzhou, People’s Republic of China
2 University of South China, Department of Nuclear Science and Technology, Hengyang, People’s Republic of China (GRID:grid.412017.1) (ISNI:0000 0001 0266 8918)
3 Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Department of Radiation Therapy, Hangzhou, People’s Republic of China (GRID:grid.412017.1)
4 Shanghai Pulmonary Hospital, Department of Radiation Therapy, Shanghai, People’s Republic of China (GRID:grid.412532.3)
5 Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Department of Radiation Therapy, Hangzhou, People’s Republic of China (GRID:grid.412532.3)