Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy q

被引：0
|
作者：
Song, Ying [1 ,2 ]
Hu, Junjie [1 ]
Wu, Qiang [2 ,3 ]
Xu, Feng [2 ,3 ]
Nie, Shihong [2 ]
Zhao, Yaqin [2 ]
Bai, Sen [2 ]
Yi, Zhang [1 ]
机构：
[1] Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, 24,South Sect 1 First Ring Rd, Chengdu 610065, Peoples R China
[2] Sichuan Univ, West China Hosp, Dept Radiotherapy, 37 Guo Xue Alley, Chengdu 610065, Peoples R China
[3] Sichuan Univ, West China Hosp, Lung Canc Ctr, Chengdu, Peoples R China
来源：
RADIOTHERAPY AND ONCOLOGY | 2020年 / 145卷
基金：
中国国家自然科学基金;
关键词：
AUTO-SEGMENTATION; CONSENSUS GUIDELINES; PELVIC VOLUMES; NORMAL TISSUE; CT IMAGES; ATLAS; VARIABILITY; VALIDATION; HEAD;
D O I：
暂无
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
引用
收藏
页码：186 / 192
页数：7
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