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Generation of annotated multimodal ground truth datasets for abdominal medical image registration

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method...

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Bibliographische Detailangaben
Hauptverfasser: Bauer, Dominik F. (VerfasserIn) , Russ, Tom (VerfasserIn) , Waldkirch, Barbara (VerfasserIn) , Tönnes, Christian (VerfasserIn) , Segars, William P. (VerfasserIn) , Schad, Lothar R. (VerfasserIn) , Zöllner, Frank G. (VerfasserIn) , Golla, Alena-Kathrin (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 02 May 2021
In: International journal of computer assisted radiology and surgery
Year: 2021, Jahrgang: 16, Heft: 8, Pages: 1277-1285
ISSN:1861-6429
DOI:10.1007/s11548-021-02372-7
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11548-021-02372-7
Volltext
Verfasserangaben:Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch, Christian Tönnes, William P. Segars, Lothar R. Schad, Frank G. Zöllner, Alena-Kathrin Golla
Beschreibung
Zusammenfassung:Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets.
Beschreibung:Gesehen am 12.12.2022
Beschreibung:Online Resource
ISSN:1861-6429
DOI:10.1007/s11548-021-02372-7

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