U‐Net based deep learning bladder segmentation in CT urography

Objectives To develop a U‐Net–based deep learning approach (U‐DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer‐assisted bladder cancer detection and treatment response assessment pipeline. Materials and methods A dataset of 173 cases including 81 cases in t...

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Vydané v:	Medical physics (Lancaster) Ročník 46; číslo 4; s. 1752 - 1765
Hlavní autori:	Ma, Xiangyuan, Hadjiiski, Lubomir M., Wei, Jun, Chan, Heang‐Ping, Cha, Kenny H., Cohan, Richard H., Caoili, Elaine M., Samala, Ravi, Zhou, Chuan, Lu, Yao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.04.2019
Predmet:	Algorithms bladder Case-Control Studies computer‐aided detection CT urography Deep Learning Humans Image Processing, Computer-Assisted - methods Neural Networks, Computer segmentation Tomography, X-Ray Computed - methods Urinary Bladder - diagnostic imaging Urinary Bladder Neoplasms - diagnostic imaging Urography - methods CT urography deep learning segmentation computer-aided detection bladder
ISSN:	0094-2405, 2473-4209, 2473-4209
On-line prístup:	Získať plný text
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