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  4. Can complex-valued neural networks improve force sensing with optical coherence tomography?
 
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Can complex-valued neural networks improve force sensing with optical coherence tomography?

Publikationstyp
Conference Paper
Date Issued
2024-05
Sprache
English
Author(s)
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Latus, Sarah  orcid-logo
Medizintechnische und Intelligente Systeme E-1  
Behrendt, Finn  
Medizintechnische und Intelligente Systeme E-1  
Bhattacharya, Debayan 
Medizintechnische und Intelligente Systeme E-1  
Neidhardt, Maximilian  
Medizintechnische und Intelligente Systeme E-1  
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
https://hdl.handle.net/11420/49103
Citation
21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Contribution to Conference
21st IEEE International Symposium on Biomedical Imaging, ISBI 2024  
Publisher DOI
10.1109/ISBI56570.2024.10635602
Scopus ID
2-s2.0-85203338960
Publisher
IEEE
ISSN
19457928
ISBN
979-8-3503-1334-5
979-8-3503-1333-8
Deep learning has proven highly effective in processing optical coherence tomography (OCT) images. However, current approaches often treat complex OCT data as real-valued representations and mostly focus on the amplitude component. In contrast, OCT phase can detect submicrometer displacements, surpassing the resolution of amplitude-based methods. In this study, we investigate complex-valued and real-valued neural networks (cvNNs and rvNNs) for end-to-end needle tip force estimation from complex OCT data. We consider predicting absolute forces and force differences over time. We compare cvNNs with two rvNNs where amplitude and phase are either stacked in the channel dimension or processed separately in a dual-path architecture. Our results show that the amplitude-focused estimation of the absolute force is still best achieved with rvNNs. However, when estimating small force differences, where phase-sensitive OCT is particularly valuable, cvNNs outperform rvNNs with stacked inputs. Similar improvements can be achieved with separate processing in the dual-path models. The results emphasize the importance of model design in processing complex OCT signals and demonstrate the potential of cvNNs for phase-sensitive OCT. However, our work also highlights the current limitations of cvNNs, particularly computational cost and data dependency.
Subjects
Complex Deep Networks
Complex-valued
Needle Insertions
Optical Sensors
Phase-sensitive
MLE@TUHH
DDC Class
610: Medicine, Health
Funding(s)
Zeitbeschränktes Model-Checking und verifikationsbewusster Systementwurf für medizinische cyber-physikalische Systeme  
TUHH
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