Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

Journal: Acta oncologica (Stockholm, Sweden)

Published Date: Feb 1, 2019

Abstract

BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compared with those generated through atlas-based automatic segmentation.

Authors

Jinhan Zhu

a State Key Laboratory of Oncology in South China , Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou , China.
Jun Zhang

First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Bo Qiu

a State Key Laboratory of Oncology in South China , Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou , China.
Yimei Liu

a State Key Laboratory of Oncology in South China , Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou , China.
Xiaowei Liu

Greater Bay Area Center for Drug Evaluation and Inspection of National Medical Products Administration, Shenzhen 518017, China.
Lixin Chen

Keywords

Anatomy, Artistic Atlases as Topic Esophagus Humans Image Processing, Computer-Assisted Lung Neoplasms Neural Networks, Computer Organs at Risk Pattern Recognition, Automated Radiography, Abdominal Radiography, Thoracic Radiotherapy Planning, Computer-Assisted Spine Tomography, X-Ray Computed Tumor Burden

External Resources

View on PubMed Access via DOI PubMed (30398090)

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals