Feasibility study on automatic segmentation of pelvic intestinal tube for radiotherapy images based on deep learning
10.3969/j.issn.1672-8467.2024.02.015
- VernacularTitle:深度学习对放疗影像进行盆腔肠管自动勾画的可行性研究
- Author:
Yu-Jie ZHANG
1
;
Xu YUAN
;
Han XIAO
;
Jian-Ying ZHANG
Author Information
1. 复旦大学附属中山医院放疗科 上海 200032
- Keywords:
rectal cancer;
small intestine tube;
automatic contouring;
deep learning
- From:
Fudan University Journal of Medical Sciences
2024;51(2):243-248
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the feasibility on automatic contouring of pelvic intestinal tube based on deep learning for radiotherapy images.Methods A total of 100 patients with diagnosis of rectal cancer,received radiotherapy in Zhongshan Hospital,Fudan University from 2019 to 2021,were randomly selected.Sixty cases were randomly enrolled to train the models,and the other 40 cases were applied to test.Based on the original small intestine model in automatic segmentation software AccuContour,60,40 and 20(2 groups)cases in the model cases were used to train the models Rec60,Rec40,Rec20A and Rec20B with manual contouring as ground truth.Other 40 cases for test were applied to evaluate the Dice similarity coefficient(DSC),95%Hausdorff distance(HD95)and average symmetric surface distance(ASSD)between the manual contouring and original model along with model Rec60.The DSC of the 5 groups of auto-segmentations were compared as well.The paired t tests were performed for each pair of the original model and 4 trained models.Results The small bowel contoured by trained models were more similar to the manual contouring.They could distinguish the boundary of the intestinal tube better and distinguish the small bowel from the colon.The average DSC,HD95 and ASSD of Rec60 were 0.16 higher(P＜0.001),12.4 lower(P＜0.001)and 5.14 lower(P＜0.001)than the original model respectively.According to the paired t tests,there were no statistical differences in DSC between the 4 training models and the original model.No statistical difference was observed between Rec60 and Rec40,while they were both significantly different from the two Rec20 models.There was no statistical difference between Rec20B and Rec20B.Conclusion For radiotherapy images,model training can effectively improve the accuracy of intestinal tube delineation.Forty cases were enough for training an optimal model of automatic segmentation for pelvic intestinal tube in AccuContour software.
