Spatio-temporal deep learning for medical image sequences

Publikationstyp
Doctoral Thesis
Date Issued
2023
Sprache
English
Author(s)
Bengs, Marcel  
Advisor
Schlaefer, Alexander  
Referee
Grigat, Rolf-Rainer  
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2023-11-10
Institute
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.8891
TORE-URI
https://hdl.handle.net/11420/44429
Citation
Technische Universität Hamburg (2023)
In this work, we study and present spatio-temporal deep learning methods for analyzing medical image sequences. We focus on two application scenarios, motion analysis and dynamic elastography, using optical coherence tomography and ultrasound as imaging modalities. Our findings show that deep learning can effectively address end-to-end processing of sequences of medical image data, including sequences of volumetric images.
Subjects
Deep Learning
Medical Image Sequences
Spatio-Temporal Data
4D Data
Elastography
Motion Analysis
DDC Class
004: Computer Sciences
610: Medicine, Health
Lizenz
http://rightsstatements.org/vocab/InC/1.0/
Publisher‘s Creditline
In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Hamburg University of Technology’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation
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