Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning-Reference-Cited by-同舟云学术

Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning

Published:2023-01 Issue: Volume:136 Page:106261
ISSN:1368-8375
Container-title:Oral Oncology
language:en
Short-container-title:Oral Oncology

Author:

Peng Yinglin,Liu Yimei,Shen Guanzhu,Chen Zijie,Chen Meining,Miao Jingjing,Zhao Chong,Deng Jincheng,Qi Zhenyu,Deng Xiaowu

Funder

Guangzhou Science and Technology Program key projects

China International Medical Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Cancer Research,Oncology,Oral Surgery

Reference34 articles.

1. Long-term late toxicities and quality of life for survivors of nasopharyngeal carcinoma treated with intensity-modulated radiotherapy versus non-intensity-modulated radiotherapy;Huang;Head Neck,2016

2. Long term results of postoperative Intensity-Modulated Radiation Therapy (IMRT) in the treatment of Squamous Cell Carcinoma (SCC) located in the oropharynx or oral cavity;Hoffmann;Radiat Oncol,2015

3. Long-term disease-specific and cognitive quality of life after intensity-modulated radiation therapy: a cross-sectional survey of nasopharyngeal carcinoma survivors;Kiang;Radiat Oncol,2016

4. Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer;Nelms;Int J Radiat Oncol Biol Phys,2012

5. Dosimetric impact of inter-observer variability for 3D conformal radiotherapy and volumetric modulated arc therapy: the rectal tumor target definition case;Lobefalo;Radiat Oncol,2013

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique;Radiography;2024-03

2. Application of FGD-BCEL loss function in segmenting temporal lobes on localized CT images for radiotherapy;Frontiers in Oncology;2023-10-05

3. Enhancing Maternofetal Ultrasound Images Toward Boosting Classification Performance on a Diverse and Comprehensive Data;2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA);2023-09-20

4. Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma;Journal of Clinical Medicine;2023-04-24