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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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Bibliographic Details
Main Authors: Chen, Shuqing (Author) , Zhong, Xia (Author) , Hu, Shiyang (Author) , Dorn, Sabrina (Author) , Kachelrieß, Marc (Author) , Lell, Michael (Author) , Maier, Andreas (Author)
Format: Article (Journal)
Language:English
Published: 1 January 2020
In: Medical physics
Year: 2020, Volume: 47, Issue: 2, Pages: 552-562
ISSN:2473-4209
DOI:10.1002/mp.13950
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/mp.13950
Verlag, lizenzpflichtig, Volltext: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.13950
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Author Notes:Shuqing Chen, Xia Zhong, Shiyang Hu, Sabrina Dorn, Marc Kachelrieß, Michael Lell, Andreas Maier
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Summary: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.
Item Description:Gesehen am 31.03.2020
Physical Description:Online Resource
ISSN:2473-4209
DOI:10.1002/mp.13950

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