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Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks

Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single-energy CT (SECT). Recent...

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Autori principali: Chen, Shuqing (Autore) , Zhong, Xia (Autore) , Hu, Shiyang (Autore) , Dorn, Sabrina (Autore) , Kachelrieß, Marc (Autore) , Lell, Michael (Autore) , Maier, Andreas (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 1 January 2020
In: Medical physics
Year: 2020, Volume: 47, Fascicolo: 2, Pages: 552-562
ISSN:2473-4209
DOI:10.1002/mp.13950
Accesso online:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/mp.13950
Verlag, lizenzpflichtig, Volltext: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.13950
Testo
Note sull'autore:Shuqing Chen, Xia Zhong, Shiyang Hu, Sabrina Dorn, Marc Kachelrieß, Michael Lell, Andreas Maier
Descrizione
Riassunto:Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single-energy CT (SECT). Recent research shows that automatic multi-organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT.
Descrizione del documento:Gesehen am 31.03.2020
Descrizione fisica:Online Resource
ISSN:2473-4209
DOI:10.1002/mp.13950

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