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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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Main Authors: Bauer, Dominik F. (Author) , Russ, Tom (Author) , Waldkirch, Barbara (Author) , Tönnes, Christian (Author) , Segars, William P. (Author) , Schad, Lothar R. (Author) , Zöllner, Frank G. (Author) , Golla, Alena-Kathrin (Author)
Format: Article (Journal)
Language:English
Published: 02 May 2021
In: International journal of computer assisted radiology and surgery
Year: 2021, Volume: 16, Issue: 8, Pages: 1277-1285
ISSN:1861-6429
DOI:10.1007/s11548-021-02372-7
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11548-021-02372-7
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Author Notes:Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch, Christian Tönnes, William P. Segars, Lothar R. Schad, Frank G. Zöllner, Alena-Kathrin Golla
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Summary: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.
Item Description:Gesehen am 12.12.2022
Physical Description:Online Resource
ISSN:1861-6429
DOI:10.1007/s11548-021-02372-7

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