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Item type:Publication, Access status: with files , Robust motion tracking and classification of zebrafish with deep learning(IEEE, 2026-09-01); ; ; ; Zebrafish serve as a key pre-clinical model for disease research. Studying disease progression requires long-term tracking of fish motion, particularly when considering the function of the musculoskeletal apparatus. While vision-based tracking has been studied, current approaches typically require substantial fine tuning and often lack robustness, especially when multiple fish need to be tracked and motion occurs along all three spatial dimensions. We propose a simplified setup and a 3D tracking method using only two cameras and two convolutional neural networks (CNN) to localize and track multiple fish robustly. We separate the localization of fish and their classification as an individual. This allows for simple training data acquisition by placing fish into the basin individually, without the need for manual annotation. We evaluate the approach on sets of two, five, and ten fish resulting in multiple object tracking accuracies (MOTA) of 0.997, 0.994, and 0.966, respectively. The mean MOTA over all scenarios is 0.99 compared to a mean MOTA of 0.89 for a state-of-the-art tracking algorithm (idtracker.ai). Finally, we present comprehensive data on the motion activity of individual fish in a 30-minute video sequence, demonstrating robust tracking and unique activity profiles of each fish.Publicationtype: Journal Article30 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 6D robotic OCT scanning of curved tissue surfacesOptical coherence tomography (OCT) is a non-invasive volumetric imaging modality with high spatial and temporal resolution. Imaging larger tissue regions requires moving the OCT probe to cover the target area. Handheld scanning approaches rely on overlapping volumes for stitching, whereas, robotic scanning approaches often restrict motion to pure translations to avoid full hand-eye calibration - a challenging problem given the OCT probe's small field of view. These registration- or translation-based stitching strategies, however, struggle when imaging curved tissue surfaces. We propose a fiducial marker enabling full six-degree-of-freedom hand-eye calibration for a robot-mounted OCT probe. Our method produces highly repeatable transform estimates and, in experiments on two phantom surfaces, enables consistent robotic scanning of large, curved geometries. Because the approach does not depend on image-based registration, it avoids error accumulation along scanning paths and outperforms conventional purely translational robotic scanning.Publicationtype: Conference Paper9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Spatiotemporal remodeling of bone as a reversibly adaptive biological material in Djungarian hamsters under regulated photoperiod conditions(Elsevier, 2026-02-12); ; ; 9 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Gender and age-related decline in lower limb standing muscle strength: benchmarking for rehabilitation assessment(Multidisciplinary Digital Publishing Institute (MDPI), 2025-12-22); ; This study aimed to demonstrate a novel sensor-based measuring stand for objective assessment of multi-directional lower limb muscle strength and to establish essential, age- and gender-stratified normative benchmarks. This cross-sectional study measured relative leg strength (N/kg) across six standing movements in 99 healthy, non-professional athletes (males and females aged 20–30, 40–50, and 60–70 years). Results confirmed that men exhibited significantly greater strength than women across all six directions (17% to 35% difference). Furthermore, a marked age-related decline was consistently observed in both sexes, with the largest and most clinically relevant differences (often exceeding 30%) concentrated in the transition to the 60–70-year range. Methodologically, these findings are limited to demonstrating age-related differences rather than longitudinal decline and are specific to an active, healthy cohort. This study demonstrates the sensor-based stand as an efficient, objective tool for comprehensive strength assessment, but its clinical utility is prospective and requires further validation against diverse and pathological patient populations.Publicationtype: Journal Article10 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust tracking with particle filtering for fluorescent cardiac imaging(De Gruyter, 2025-12-01); ; ; ; Intraoperative fluorescent cardiac imaging enables quality control following coronary bypass grafting surgery.We can estimate local quantitative indicators, such as cardiac perfusion, by tracking local feature points. However, heart motion and significant fluctuations in image characteristics caused by vessel structural enrichment limit traditional tracking methods. We propose a particle filtering tracker based on cyclicconsistency checks to robustly track particles sampled to follow target landmarks. Our method tracks 117 targets simultaneously at 25.4 fps, allowing real-time estimates during interventions. It achieves a tracking error of (5.00 ± 0.22 px) and outperforms other deep learning trackers (22.3 ± 1.1 px) and conventional trackers (58.1 ± 27.1 px).Publicationtype: Journal Article11 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A provably safe controller for the needle-steering problem using online strategy synthesisAutonomous systems often address complex planning problems, which require both prospective action planning and retrospective data evaluation. Timed games could aid since they automatically synthesize strategies that, provably correct, solve those planning problems; yet, they assume a static model of the environment, which is not realistic for autonomous systems. However, many autonomous systems are control applications, which employ sensors that capture system behavior at run time and can thus compensate for incomplete knowledge at modeling time. In this paper, we propose an online strategy synthesis, which, based on offline strategy synthesis on the one hand and on sensor information about the current state of the physical world on the other hand, derives formal safety guarantees while reacting and adapting to environment changes. We formalize the needle-steering problem from medical robotics, i.e., the problem of navigating a (flexible and beveled) needle through partially unknown tissue towards a target without damaging its surroundings, by interpreting it as a timed game. Further, we introduce a new representation of its environment through different region types that determine the acceptance of action plans and trigger local correcting actions. We present an algorithm for online strategy synthesis and, for the given region representation, formally prove that it returns safe online controllers. The algorithm is implemented on top of Uppaal Stratego. For two medical applications of needle steering, peridural anesthesia and predefined needle trajectory, we demonstrate the necessity of online adjustments in a series of simulations with various degrees of initial knowledge about the environment, and show that the overhead of online synthesis remains practical.Publicationtype: Journal Article36 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A digital twin for robotic post mortem tissue sampling using virtual reality(IEEE, 2025-09-10); ; ; ; Studying tissue samples obtained during autopsies is the gold standard when diagnosing the cause of death and for understanding disease pathophysiology. Recently, the interest in post mortem minimally invasive biopsies has grown which is a less destructive approach in comparison to an open autopsy and reduces the risk of infection. While manual biopsies under ultrasound guidance are more widely performed, robotic post mortem biopsies have been recently proposed. This approach can further reduce the risk of infection for physicians. However, planning of the procedure and control of the robot need to be efficient and usable. We explore a virtual reality setup with a digital twin to realize fully remote planning and control of robotic post mortem biopsies. The setup is evaluated with forensic pathologists in a usability study for three interaction methods. Furthermore, we evaluate clinical feasibility and evaluate the system with three human cadavers. Overall, 132 needle insertions were performed with an off-axis needle placement error of 5.30±3.25 mm. Tissue samples were successfully biopsied and histopathologically verified. Users reported a very intuitive needle placement approach, indicating that the system is a promising, precise, and low-risk alternative to conventional approaches.Publicationtype: Journal Article46 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust tracking with particle filtering for fluorescent cardiac imagingIntraoperative fluorescent cardiac imaging enables quality control following coronary bypass grafting surgery. We can estimate local quantitative indicators, such as cardiac perfusion, by tracking local feature points. However, heart motion and significant fluctuations in image characteristics caused by vessel structural enrichment limit traditional tracking methods. We propose a particle filtering tracker based on cyclicconsistency checks to robustly track particles sampled to follow target landmarks. Our method tracks 117 targets simultaneously at 25.4 fps, allowing real-time estimates during interventions. It achieves a tracking error of (5.00 +/- 0.22 px) and outperforms other deep learning trackers (22.3 +/- 1.1 px) and conventional trackers (58.1 +/- 27.1 px).Publicationtype: Preprint21 12 - 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