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  4. Deep learning based segmentation of cervical blood vessels in ultrasound images
 
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Deep learning based segmentation of cervical blood vessels in ultrasound images

Publikationstyp
Conference Paper
Date Issued
2022-06
Sprache
English
Author(s)
Sonntag, Tim  
Bauer, Marcus  
Sprenger, Johanna  
Gerlach, Stefan  orcid-logo
Breitfeld, Philipp  
Schlaefer, Alexander  
Institut
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
http://hdl.handle.net/11420/14525
Journal
European journal of anaesthesiology  
Volume
39
Issue
e-Supplement 60
Start Page
41
End Page
41
Article Number
01AP08-08
Citation
The European Anaesthesiology Congress (Euroanaesthesia 2022)
Contribution to Conference
The European Anaesthesiology Congress, Euroanaesthesia 2022  
Publisher Link
https://www.esaic.org/uploads/2022/06/esaic2022_abstracts.pdf
Puncture of central vessels is a frequently used therapeutic and diagnostic procedure. The use of ultrasound (US) during needle insertion has become the gold standard. Handling the US probe and needle is challenging, especially in difficult anatomic conditions. Our long-term vision is a deep learning based and augmented reality (AR) assisted needle puncture. We aim to visualize the vessel structures in 3D based on 2D US image segmentation. While punctuating, the relative needle tip position and relevant vessels can be highlighted via AR lenses to optimize the image guidance process
Subjects
MLE@TUHH
DDC Class
610: Medizin
TUHH
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