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Item type:Publication, Access status: with files , Feedback on tool-tissue interactions with optical coherence tomography and deep learningThis thesis investigates the use of optical coherence tomography (OCT) for sensing tool-tissue interactions in robotic interventions. It explores the integration of miniaturized OCT with collaborative robotic systems to enable real-time, localized feedback at the needle tip. Additionally, it examines the application of deep learning adapted to spatio-temporal OCT data to enhance sensing accuracy and robustness. The results demonstrate the potential of OCT-based feedback for advancing intelligent surgical systems.Publicationtype: Doctoral Thesis51 53 - 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 Paper8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A-scan sequence transformers for palpation with optical coherence elastography(2025-05-01); ; ; ; Recognizing the properties of elastic tissue can facilitate surgical navigation, e.g., when localizing lesions by palpation. However, palpation is very subjective and often unavailable in minimally invasive surgery. High-speed optical coherence elastography (OCE) adapted for intraoperative use could enable elasticity estimation by measuring the propagation of mechanically stimulated waves. However, robust estimation of wave velocity can be challenging, and reconstruction of the elastic modulus is highly dependent on the correct modeling of wave propagation. We therefore consider deep learning for the end-to-end estimation of elasticity from OCE phase data. Since optical coherence tomography inherently produces a temporal sequence of one-dimensional axial scans (A-scans), we consider transformer-based deep learning models to directly process A-scan sequences. For homogeneous tissue phantoms with known elastic properties, we obtain a mean error of 1.64 kPa, which significantly improves elasticity reconstruction compared to conventional processing and the best CNN-based approach with 7.80 kPa and 5.55 kPa, respectively. Furthermore, we demonstrate generalization to heterogeneous phantoms with inclusions and assess the elasticity of soft tissue samples, including heart, kidney, and liver. The results show that transformer architectures are well suited for reconstructing elasticity from A-scan sequences in OCE.Publicationtype: Journal Article26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs(2025-01-22); ; ; ; The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image. As these models should fail to reconstruct unhealthy structures, the reconstruction errors indicate anomalies. However, a significant challenge is to balance the accurate reconstruction of healthy anatomy and the undesired replication of abnormal structures. While diffusion models have shown promising results with detailed and accurate reconstructions, they face challenges in preserving intensity characteristics, resulting in false positives. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image. We demonstrate that this conditioning allows for accurate and local adaptation to the general input intensity distribution while avoiding the replication of unhealthy structures. We compare the novel approach to different state-of-the-art methods and for different data sets. Our results show substantial improvements in the segmentation performance, with the Dice score improved by 11.9%, 20.0%, and 44.6%, for the BraTS, ATLAS and MSLUB data sets, respectively, while maintaining competitive performance on the WMH data set. Furthermore, our results indicate effective domain adaptation across different MRI acquisitions and simulated contrasts, an important attribute for general anomaly detection methods. The code for our work is available at https://github.com/FinnBehrendt/Conditioned-Diffusion-Models-UAD.Publicationtype: Journal Article39 40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A modified da Vinci surgical instrument for optical coherence elastography with deep learning(IEEE, 2024-10-23); ; ; ; Robot-assisted surgery has advantages compared to conventional laparoscopic procedures, e.g., precise movement of the surgical instruments, improved dexterity, and high-resolution visualization of the surgical field. However, mechanical tissue properties may provide additional information, e.g., on the location of lesions or vessels. While elastographic imaging has been proposed, it is not readily available as an online modality during robot-assisted surgery. We propose modifying a da Vinci surgical instrument to realize optical coherence elastography (OCE) for quantitative elasticity estimation. The modified da Vinci instrument is equipped with piezoelectric elements for shear wave excitation and we employ fast optical coherence tomography (OCT) imaging to track propagating wave fields, which are directly related to biomechanical tissue properties. All high-voltage components are mounted at the proximal end outside the patient. We demonstrate that external excitation at the instrument shaft can effectively stimulate shear waves, even when considering damping. Comparing conventional and deep learning-based signal processing results in mean absolute errors of 19.27 kPa and 6.29 kPa for a range of 17 kPa-139 kPa, respectively. These results illustrate that precise quantitative elasticity estimates can be obtained. We also demonstrate quantitative elasticity estimation on ex-vivo tissue samples of heart, liver and stomach, and show that the measurements can be used to distinguish soft and stiff tissue types.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Diffusion models with ensembled structure-based anomaly scoring for unsupervised anomaly detectionSupervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as a viable alternative for pathology segmentation, as only healthy data is required for training. However, recent UAD anomaly scoring functions often focus on intensity only and neglect structural differences, which impedes the segmentation performance. This work investigates the potential of Structural Similarity (SSIM) to bridge this gap. SSIM captures both intensity and structural disparities and can be advantageous over the classical l1 error. However, we show that there is more than one optimal kernel size for the SSIM calculation for different pathologies. Therefore, we investigate an adaptive ensembling strategy for various kernel sizes to offer a more pathology-agnostic scoring mechanism. We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.Publicationtype: Conference Paper11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Can complex-valued neural networks improve force sensing with optical coherence tomography?(IEEE, 2024-05); ; ; ; 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.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Leveraging the Mahalanobis Distance to Enhance Unsupervised Brain MRI Anomaly Detection(Springer, 2024); ; ; ; Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are detected as deviations between input and reconstruction. However, this method is sensitive to imperfect reconstructions, leading to false positives that impede the segmentation. To address this limitation, we construct multiple reconstructions with probabilistic diffusion models. We then analyze the resulting distribution of these reconstructions using the Mahalanobis distance to identify anomalies as outliers. By leveraging information about normal variations and covariance of individual pixels within this distribution, we effectively refine anomaly scoring, leading to improved segmentation. Our experimental results demonstrate substantial performance improvements across various data sets. Specifically, compared to relying solely on single reconstructions, our approach achieves relative improvements of 15.9%, 35.4%, 48.0%, and 4.7% in terms of AUPRC for the BRATS21, ATLAS, MSLUB and WMH data sets, respectively.Publicationtype: Conference Paper30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Miniature Dual-Fiber Probe for Quantitative Optical Coherence Elastography(2023-11); ; ; ; Optical coherence elastography (OCE) allows for high resolution analysis of elastic tissue properties. However, due to the limited penetration of light into tissue, miniature probes are required to reach structures inside the body, e.g., vessel walls. Shear wave elastography relates shear wave velocities to quantitative estimates of elasticity. Generally, this is achieved by measuring the runtime of waves between two or multiple points. For miniature probes, optical fibers have been integrated and the runtime between the point of excitation and a single measurement point has been considered. This approach requires precise temporal synchronization and spatial calibration between excitation and imaging. We present a miniaturized dual-fiber OCE probe of diameter allowing for robust shear wave elastography. Shear wave velocity is estimated between two optics and hence independent of wave propagation between excitation and imaging. We quantify the wave propagation by evaluating either a single or two measurement points. Particularly, we compare both approaches to ultrasound elastography. Our experimental results demonstrate that quantification of local tissue elasticities is feasible. For homogeneous soft tissue phantoms, we obtain mean deviations of and for single-fiber and dual-fiber OCE, respectively. In inhomogeneous phantoms, we measure mean deviations of up to and for single-fiber and dual-fiber OCE, respectively. We present a dual-fiber OCE approach that is much more robust in inhomogeneous tissues. Moreover, we demonstrate the feasibility of elasticity quantification in ex-vivo coronary arteries. This study introduces an approach for robust elasticity quantification from within the tissue.Publicationtype: Journal Article37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Bayesian reconstruction algorithms for low-dose computed tomography are not yet suitable in clinical context(Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-23); ; ; Computed tomography (CT) is a widely used examination technique that usually requires a compromise between image quality and radiation exposure. Reconstruction algorithms aim to reduce radiation exposure while maintaining comparable image quality. Recently, unsupervised deep learning methods have been proposed for this purpose. In this study, a promising sparse-view reconstruction method (posterior temperature optimized Bayesian inverse model; POTOBIM) is tested for its clinical applicability. For this study, 17 whole-body CTs of deceased were performed. In addition to POTOBIM, reconstruction was performed using filtered back projection (FBP). An evaluation was conducted by simulating sinograms and comparing the reconstruction with the original CT slice for each case. A quantitative analysis was performed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The quality was assessed visually using a modified Ludewig’s scale. In the qualitative evaluation, POTOBIM was rated worse than the reference images in most cases. A partially equivalent image quality could only be achieved with 80 projections per rotation. Quantitatively, POTOBIM does not seem to benefit from more than 60 projections. Although deep learning methods seem suitable to produce better image quality, the investigated algorithm (POTOBIM) is not yet suitable for clinical routine.Publicationtype: Journal Article8 45