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  4. Deep learning methods for automated segmentation of medical ultrasound images
 
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Deep learning methods for automated segmentation of medical ultrasound images

Citation Link: https://doi.org/10.15480/882.9021
Publikationstyp
Doctoral Thesis
Date Issued
2024
Sprache
English
Author(s)
Holstein, Lennart 
Advisor
Schlaefer, Alexander  
Referee
Grigat, Rolf-Rainer  
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2023-12-18
Institute
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.9021
TORE-URI
https://hdl.handle.net/11420/44941
Citation
Technische Universität Hamburg (2024)
For deep learning-based image segmentation, large training datasets are required to achieve satisfactory results. However, assembling large annotated datasets in the medical field is difficult and sometimes even impossible. Therefore, we have developed and investigated new deep learning methods that aim to improve segmentation performance with smaller ultrasound datasets. These methods include wavelet scattering, tissue shape priors with independent component analysis, topological loss functions, and synthetic data augmentation with a new generative adversarial network. The results show that the various methods offer different advantages depending on tissue type, dataset size, and CNN architecture.
Subjects
deep learning
ultrasound
segmentation
IVUS
GAN
neural network
DDC Class
004: Computer Sciences
Funding(s)
MALEKA: Maschinelle Lernverfahren für die kardiovaskuläre Bildgebung auf der Grundlage des Programms für Innovation (PROFI) - Modul PROFI Transfer Plus  
Funding Organisations
European Union  
Hamburgische Investitions- und Förderbank  
Lizenz
https://creativecommons.org/licenses/by/4.0/
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