6 results
Now showing1 - 6 of 6
- Some of the metrics are blocked by yourconsent settings
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: with files , Use of electrical impedance spectroscopy to distinguish cancer from normal tissues with a four electrode terminal setup(De Gruyter, 2020-11-26); ; ; Cancer and normal tissues are visually different from each other, especially so in more advanced cancer stages. More important, they are not only visually contrasting, but if an electric field is applied to both tissue types and the frequency is varied in a wide range, it will be seen that the two tissue types in general have a spectral response divergent from each other and this has to do with the characteristics of cancer tissues in contrast to normal ones. In this work, Electrical Impedance Spectroscopy is applied to try to distinguish cancer from healthy tissues by means of their impedance spectrum using a four-electrode-terminal setup. The use of the fourterminal- setup setup is important to circumvent the impact of electrode polarization at frequencies below 1 kHz.Publicationtype: Journal ArticleScopus© Citations 5 179 204 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Force estimation from 4D OCT data in a human tumor xenograft mouse model(De Gruyter, 2020-05-01); ; ; ; Minimally invasive robotic surgery offer benefits such as reduced physical trauma, faster recovery and lesser pain for the patient. For these procedures, visual and haptic feedback to the surgeon is crucial when operating surgical tools without line-of-sight with a robot. External force sensors are biased by friction at the tool shaft and thereby cannot estimate forces between tool tip and tissue. As an alternative, vision-based force estimation was proposed. Here, interaction forces are directly learned from deformation observed by an external imaging system. Recently, an approach based on optical coherence tomography and deep learning has shown promising results. However, most experiments are performed on ex-vivo tissue. In this work, we demonstrate that models trained on dead tissue do not perform well in in vivo data. We performed multiple experiments on a human tumor xenograft mouse model, both on in vivo, perfused tissue and dead tissue. We compared two deep learning models in different training scenarios. Training on perfused, in vivo data improved model performance by 24% for in vivo force estimation.Publicationtype: Journal ArticleScopus© Citations 1 196 191 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mass production of highly active NK cells for cancer immunotherapy in a GMP conform perfusion bioreactor(Frontiers Media S.A., 2019-08-13); ; ; ; NK cells have emerged as promising candidates for cancer immunotherapy, especially due to their ability to fight circulating tumor cells thereby preventing metastases formation. Hence several studies have been performed to generate and expand highly cytotoxic NK cells ex vivo, e.g., by using specific cytokines to upregulate both their proliferation and surface expression of distinct activating receptors. Apart from an enhanced activity, application of NK cells as immunotherapeutic agent further requires sufficient cell numbers and a high purity. All these parameters depend on a variety of different factors including the starting material, additives like cytokines as well as the culture system. Here we analyzed PBMC-derived NK cells of five anonymized healthy donors expanded under specific conditions in an innovative perfusion bioreactor system with respect to their phenotype, IFNγ production, and cytotoxicity in vitro. Important features of the meander type bioreactors used here are a directed laminar flow of medium and control of relevant process parameters. Cells are cultivated under “steady state” conditions in perfusion mode. Our data demonstrate that expansion of CD3+ T cell depleted PBMCs in our standardized system generates massive amounts of highly pure (>85%) and potent anti-cancer active NK cells. These cells express a variety of important receptors driving NK cell recruitment, adhesion as well as activation. More specifically, they express the chemokine receptors CXCR3, CXCR4, and CCR7, the adhesion molecules L-selectin, LFA-1, and VLA-4, the activating receptors NKp30, NKp44, NKp46, NKG2D, DNAM1, and CD16 as well as the death ligands TRAIL and Fas-L. Moreover, the generated NK cells show a strong IFNγ expression upon cultivation with K562 tumor cells and demonstrate a high cytotoxicity toward leukemic as well as solid tumor cell lines in vitro. Altogether, these characteristics promise a high clinical potency of thus produced NK cells awaiting further evaluation.Publicationtype: Journal ArticleScopus© Citations 17 274 418 - 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