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  4. Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks
 
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Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks

Publikationstyp
Conference Paper
Date Issued
2021
Sprache
English
Author(s)
Bargsten, Lennart 
Riedl, Katharina A.  
Wissel, Tobias  
Brunner, Fabian J.  
Schaefers, Klaus  
Sprenger, Johanna  
Grass, Michael  
Seiffert, Moritz  
Blankenberg, Stefan  
Schlaefer, Alexander  
Herausgeber*innen
Ruiter, Nicole V.  
Byram, Brett C.  
Institut
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
http://hdl.handle.net/11420/8980
First published in
Progress in Biomedical Optics and Imaging - Proceedings of SPIE  
Number in series
11602
Start Page
4
Article Number
04-7
Citation
Ultrasonic Imaging and Tomography: SPIE Medical Imaging (2021)
Contribution to Conference
SPIE Medical Imaging, 2021  
Publisher DOI
10.1117/12.2580720
Scopus ID
2-s2.0-85103465532
Publisher
SPIE
ISBN
978-1-5106-4034-4
|978-1-5106-4033-7
DDC Class
004: Informatik
600: Technik
610: Medizin
620: Ingenieurwissenschaften
Funding(s)
MALEKA: Maschinelle Lernverfahren für die kariovaskuläre Bildgebung auf der Grundlage des Programms für Innovation (PROFI) - Modul PROFI Transfer Plus  
I³-Lab - Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin  
More Funding Information
European Regional Development Fund (ERDF)
TUHH
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