Automatic segmentation of high‐risk clinical target volume for tandem‐and‐ovoids brachytherapy patients using an asymmetric dual‐path convolutional neural network-Reference-Cited by-同舟云学术

Automatic segmentation of high‐risk clinical target volume for tandem‐and‐ovoids brachytherapy patients using an asymmetric dual‐path convolutional neural network

Published:2022-02-04 Issue:3 Volume:49 Page:1712-1722
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Cao Yufeng¹,Vassantachart April²,Ragab Omar¹,Bian Shelly¹,Mitra Priya¹,Xu Zhengzheng¹,Gallogly Audrey Zhuang¹,Cui Jing¹,Shen Zhilei Liu¹,Balik Salim¹,Gribble Michael³,Chang Eric L.¹,Fan Zhaoyang¹⁴,Yang Wensha¹

Affiliation:

1. Department of Radiation Oncology Keck School of Medicine University of Southern California Los Angeles California USA

2. Department of Radiation Oncology LAC+USC Medical Center Los Angeles California USA

3. Keck School of Medicine University of Southern California Los Angeles California USA

4. Department of Radiology Keck School of Medicine University of Southern California Los Angeles California USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15490

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4. Clinical outcome and dosimetric parameters of chemo-radiation including MRI guided adaptive brachytherapy with tandem-ovoid applicators for cervical cancer patients: A single institution experience

5. Recommendations from Gynaecological (GYN) GEC-ESTRO Working Group☆ (I): concepts and terms in 3D image based 3D treatment planning in cervix cancer brachytherapy with emphasis on MRI assessment of GTV and CTV

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