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  4. A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
 
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A Deep Learning Approach for Motion Forecasting Using 4D OCT Data

Publikationstyp
Preprint
Date Issued
2020-04-21
Sprache
English
Author(s)
Bengs, Marcel  
Gessert, Nils Thorben  
Schlaefer, Alexander  
Institut
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
http://hdl.handle.net/11420/14483
Citation
arXiv: 2004.10121 (2020)
Publisher DOI
10.48550/arXiv.2004.10121
ArXiv ID
2004.10121v2
Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or motion compensation. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution. Recently, deep learning methods have shown promising performance for OCT-based motion estimation based on two volumetric images. We extend this approach and investigate whether using a time series of volumes enables motion forecasting. We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes. We design and evaluate five different 3D and 4D deep learning methods using a tissue data set. Our best performing 4D method achieves motion forecasting with an overall average correlation coefficient of 97.41%, while also improving motion estimation performance by a factor of 2.5 compared to a previous 3D approach.
Subjects
eess.IV
Computer Science - Computer Vision and Pattern Recognition
DDC Class
620: Ingenieurwissenschaften
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