Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy-Reference-Cited by-同舟云学术

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Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy

Published:2023-11 Issue:4 Volume:117 Page:994-1006
ISSN:0360-3016
Container-title:International Journal of Radiation Oncology*Biology*Physics
language:en
Short-container-title:International Journal of Radiation Oncology*Biology*Physics

Author:

Liao Wenjun,Luo Xiangde,He Yuan,Dong Ye,Li Churong,Li Kang,Zhang Shichuan,Zhang Shaoting,Wang Guotai,Xiao Jianghong

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology,Radiation

Reference37 articles.

1. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy;Chen;Radiother Oncol,2021

2. Inter-observer variability of manual contour delineation of structures in CT;Joskowicz;Eur Radiol,2019

3. Interobserver variations in the delineation of target volumes and organs at risk and their impact on dose distribution in intensity-modulated radiation therapy for nasopharyngeal carcinoma;Peng;Oral Oncol,2018

4. Variations in target volume definition for postoperative radiotherapy in stage III non-small-cell lung cancer: Analysis of an international contouring study;Spoelstra;Int J Radiat Oncol Biol Phys,2010

5. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies;Vinod;Radiother Oncol,2016

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3. Revolutionizing radiation therapy: the role of AI in clinical practice;Journal of Radiation Research;2023-11-22

4. Deep learning-assisted multiple organ segmentation from whole-body CT images;2023-10-21

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