Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and high‐quality radiation therapy planning. The purpose of this work is to develop a deep learning‐based method to automatically segment multiple thoracic OARs on chest computed tomography (CT) for radiotherapy treat...

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Veröffentlicht in:	Medical physics (Lancaster) Jg. 46; H. 5; S. 2157 - 2168
Hauptverfasser:	Dong, Xue, Lei, Yang, Wang, Tonghe, Thomas, Matthew, Tang, Leonardo, Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.05.2019
Schlagworte:	chest segmentation deep learning Esophagus - diagnostic imaging Esophagus - radiation effects Heart - diagnostic imaging Heart - radiation effects Humans Image Processing, Computer-Assisted - methods Lung - diagnostic imaging Lung - radiation effects Neoplasms - radiotherapy Organs at Risk - radiation effects Radiography, Thoracic - methods Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted - methods Radiotherapy, Intensity-Modulated - methods Spinal Cord - diagnostic imaging Spinal Cord - radiation effects Tomography, X-Ray Computed - methods chest segmentation CT deep learning
ISSN:	0094-2405, 2473-4209, 2473-4209
Online-Zugang:	Volltext
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