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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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Bibliographische Detailangaben
Hauptverfasser: Chen, Shuqing (VerfasserIn) , Zhong, Xia (VerfasserIn) , Hu, Shiyang (VerfasserIn) , Dorn, Sabrina (VerfasserIn) , Kachelrieß, Marc (VerfasserIn) , Lell, Michael (VerfasserIn) , Maier, Andreas (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 1 January 2020
In: Medical physics
Year: 2020, Jahrgang: 47, Heft: 2, Pages: 552-562
ISSN:2473-4209
DOI:10.1002/mp.13950
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/mp.13950
Verlag, lizenzpflichtig, Volltext: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.13950
Volltext
Verfasserangaben:Shuqing Chen, Xia Zhong, Shiyang Hu, Sabrina Dorn, Marc Kachelrieß, Michael Lell, Andreas Maier
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 31.03.2020
Beschreibung:Online Resource
ISSN:2473-4209
DOI:10.1002/mp.13950

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