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: | , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
02 May 2021
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| 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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| 520 | |a 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. | ||
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