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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Medical physics (Lancaster) Ročník 46; číslo 5; s. 2157 - 2168
Hlavní autori:	Dong, Xue, Lei, Yang, Wang, Tonghe, Thomas, Matthew, Tang, Leonardo, Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.05.2019
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!