|Table of Contents|

Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for rectal cancer based on deep learning

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2024 19
Page:
3757-3762
Research Field:
Publishing date:

Info

Title:
Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for rectal cancer based on deep learning
Author(s):
LI Linshan1; SI Mengyuan1; SU Kunpu1; XIAO Yao1; ZHOU Deli1; LIU Yanhai1; ZHOU Peng1; LUO Jia1; HU Hai2; LI Mengxia1; CHEN Chuan1
1.Department of Oncology，Army Specialized Medical Center，Chongqing 400042，China；2.Institute of Physics，Chinese Academy of Sciences，Beijing 100190，China.
Keywords:
deep learning; rectal cancer; radiotherapy; automatic segmentation
PACS:
R735.3
DOI:
10.3969/j.issn.1672-4992.2024.19.023
Abstract:
Objective:To explore the feasibility of applying an improved Unet convolutional neural network model to the segmentation of target areas and organs at risk in radiotherapy for rectal cancer.Methods:This study involved a retrospective analysis of data from 120 rectal cancer patients.A random selection of 80 cases were used for the training set,20 for the validation set,and 20 for the test set.The targets for automatic delineation included the rectal cancer clinical target volume(CTV),left and right femoral heads,and the bladder.The network model's automatic delineations were compared against manual delineations by clinical experts and the atlas-based automatic segmentation technique(ABAS).Evaluation metrics such as DSC coefficients,Hausdorff distances,and intersection over union(IoU) were employed.Results:The deep learning model yielded DSC coefficients for CTV,bladder,left femoral head,and right femoral head of 0.90±0.06,0.95±0.11,0.98±0.01,and 0.96±0.05,respectively.The 95% Hausdorff distances were (7.58±4.70)mm,(4.11±8.58)mm,(1.37±2.09)mm,and (1.50±2.19)mm,respectively.The IoU values were 0.82±0.09,0.91±0.13,0.96±0.03,and 0.94±0.06,respectively.In comparison,ABAS yielded DSC coefficients for CTV,bladder,left and right femoral heads of 0.83±0.13,0.68±0.27,0.89±0.12,and 0.88±0.13.95%HD values of (5.78±7.55)mm,(13.81±15.76)mm,(1.93±3.23)mm,and （2.13±3.70）mm and IoU values of 0.73±0.15,0.57±0.27,0.81±0.14,and 0.80±0.15.Conclusion:The improved Unet convolutional neural network model demonstrated high accuracy in the automatic delineation of CTV and organs-at-risk in rectal cancer.Its application in clinical settings can significantly enhance the efficiency and consistency of delineations made by medical professionals,thereby contributing to the precision of radiotherapy treatments,this study also paves the way for automated radiotherapy planning.

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重庆市科卫联合医学科研项目重点项目（编号：2022ZDXM027）
Last Update: 2024-08-30