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Item type:Publication, Access status: without files , Deep denoising of volumetric OCT images for in vivo motion detection(IEEE, 2025-07); ; ; ; 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.Publicationtype: Conference Paper7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs(SPIE, 2021); ; ; ; Precise navigation is an important task in robot-assisted and minimally invasive surgery. The need for optical markers and a lack of distinct anatomical features on skin or organs complicate tissue tracking with commercial tracking systems. Previous work has shown the feasibility of a 3D optical coherence tomography based system for this purpose. Furthermore, convolutional neural networks have been proven to precisely detect shifts between volumes. However, most experiments have been performed with phantoms or ex-vivo tissue. We introduce an experimental setup and perform measurements on perfused and non-perfused (dead) tissue of in-vivo xenograft tumors. We train 3D siamese deep learning models and evaluate the precision of the motion prediction. The network\'s ability to predict shifts for different motion magnitudes and also the performance for the different volume axes are compared. The root-mean-square errors are 0:12mm and 0:08mm on perfused and non-perfused tumor tissue, respectivelyPublicationtype: Conference Paper91 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Electrical Impedance Spectroscopy for Characterization of Prostate PC-3 and du 145 Cancer CellsThe impedance profile of the human PC-3 and DU 145 prostate cancer cells were recorded and compared using Electrical Impedance Spectroscopy. Cells were measured in a special chamber using a four terminal setup to avoid parasitic effects of electrode polarization in low frequencies. Our results show that the two cancer cell lines are readily distinguishable by their impedance spectrum. As PC-3 cells have been shown to be spontaneously metastatic in previous xenograft experiments while DU 145 cells were non-metastatic, Electrical Impedance Spectroscopy has the potential to be developed into a simple diagnostic tool to distinguish metastatic from non-metastatic cells.Publicationtype: Conference Paper331