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  4. Deep denoising of volumetric OCT images for in vivo motion detection
 
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Deep denoising of volumetric OCT images for in vivo motion detection

Publikationstyp
Conference Paper
Date Issued
2025-07
Sprache
English
Author(s)
Sprenger, Johanna  
Medizintechnische und Intelligente Systeme E-1  
Neidhardt, Maximilian  
Medizintechnische und Intelligente Systeme E-1  
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Behrendt, Finn  
Medizintechnische und Intelligente Systeme E-1  
Schlüter, Matthias  
Medizintechnische und Intelligente Systeme E-1  
Latus, Sarah  orcid-logo
Medizintechnische und Intelligente Systeme E-1  
Mia Beine
Gosau, Tobias  
Kemmling, Julia  
Valentiner, Ursula  
Schumacher, Udo  
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
https://hdl.handle.net/11420/60397
Start Page
1
End Page
6
Citation
47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025
Contribution to Conference
47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2025  
Publisher DOI
10.1109/embc58623.2025.11253473
Publisher
IEEE
ISBN of container
979-8-3315-8619-5
979-8-3315-8618-8
Optical Coherence Tomography (OCT) is an imaging modality with high temporal and spatial resolution. There are different applications, e.g. in ophthalmology, dermatology, cardiology and recently OCT image based motion compensation has been considered. However, OCT suffers from speckle noise which affects image quality and subsequent interpretation. We propose denoising based on unsupervised 3D convolutional Autoencoder (AE) and systematically evaluate the model on in vivo and post mortem datasets. Moreover, we study motion detection as a subsequent task and to investigate the effects of AE based denoising. Our results demonstrate that speckle noise in the OCT images can lead to substantial outliers. Denoising based on AEs is effective in reducing the outliers and results in improved motion detection. Furthermore, the proposed AE processes OCT volumes in 0.5 ms, making it suitable for real-time applications. Finally, the results illustrate that the AE can effectively improve motion detection performance on in vivo data, despite being trained on different data. In conclusion, our AE model presents a simple and unsupervised deep learning approach to obtain fast denoising adapted to OCT imaging. For motion detection, denoising can be crucial to avoid artifacts due to outliers.Clinical relevance: This work provides an efficient denoising Autoencoder for fast data processing that can be applied in various clinical scenarios to improve noisy 3D Optical Coherence Tomography images.
Subjects
In vivo
Three-dimensional displays
Optical coherence tomography
Noise reduction
Noise
Autoencoders
Imaging
Speckle
Motion detection
Spatial resolution
DDC Class
610: Medicine, Health
Funding(s)
Mechatronically guided micro navigation for soft tissue needle insertion  
Centre of Excellence of Al for Sustainable Living and Working  
TUHH
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