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Item type:Publication, Access status: with files , An AI based mobile robot for autonomous ultrasound in legal medicineComprehensive documentation of internal tissue structures plays an important role in legal medicine. Although ultrasound is widely used in medical diagnostics as a powerful modality for injury detection and documentation, its integration into legal medicine workflows remains limited. Potential reasons include physical strain and infection risks due to manual probe placement at different locations along the body. Robotic ultrasound systems have been proposed to address such challenges. However, current systems are typically restricted to specific target regions and are limited in mobility and autonomy, complicating their clinical integration and fully autonomous, task-level operation. We therefore propose a mobile robotic system for autonomous ultrasound scanning across the body. The system localizes the body, estimates internal target structures, moves to reach the target structure, and performs ultrasound scans. We evaluate the system in three stages. First, we assess the accuracy and robustness of target localization and scan path planning across different body types. Second, we conduct experiments with a body phantom to evaluate the repeatability of covering target structures. Third, we demonstrate the system in a real legal medicine setting. Our experiments demonstrate that the system can approach the body, define the scan path, and acquire reproducible ultrasound images of target structures. These results demonstrate the feasibility of fully autonomous ultrasound imaging across multiple anatomical regions using a mobile robotic system. Beyond legal medicine, the proposed mobile system offers perspectives for autonomous imaging in broader clinical settings such as telemedicine and infectious disease settings, supporting remote diagnostics while reducing infection risks.Publicationtype: Journal Article11 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Do we need pre-processing for deep learning based ultrasound shear wave elastography?(De Gruyter, 2026-01-20); ; ; ; Estimating the elasticity of soft tissue can provide useful information for various diagnostic applications. Ultrasound shear wave elastography offers a non-invasive approach. However, its generalizability and standardization across different systems and processing pipelines remain limited. Considering the influence of image processing on ultrasound based diagnostics, recent literature has discussed the impact of different image processing steps on reliable and reproducible elasticity analysis. In this work, we investigate the need of ultrasound pre-processing steps for deep learning-based ultrasound shear wave elastography. We evaluate the performance of a 3D convolutional neural network in predicting shear wave velocities from spatio-temporal ultrasound images, studying different degrees of pre-processing on the input images, ranging from fully beamformed and filtered ultrasound images to raw radiofrequency data. We compare the predictions from our deep learning approach to a conventional timeof- flight method across four gelatin phantoms with different elasticity levels. Our results demonstrate statistically significant differences in the predicted shear wave velocity among all elasticity groups, regardless of the degree of pre-processing. Although pre-processing slightly improves performance metrics, our results show that the deep learning approach can reliably differentiate between elasticity groups using raw, unprocessed radiofrequency data. These results show that deep learning-based approaches could reduce the need and the bias of traditional ultrasound pre-processing steps in ultrasound shear wave elastography, enabling faster and more reliable clinical elasticity assessments.Publicationtype: Journal Article22 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A mobile robotic approach to autonomous surface scanning in legal medicinePurpose : Comprehensive legal medicine documentation includes internal and external examination of the corpse. Typically, this documentation is conducted manually during conventional autopsy. Systematic digital documentation would be desirable, especially for external wound examination, which is becoming more relevant for legal medicine analysis. For this purpose, RGB surface scanning has been introduced. While manual full-surface scanning using a handheld camera is time-consuming and operator-dependent, floor or ceiling-mounted robotic systems require specialized rooms. Hence, we consider whether a mobile robotic system can be used for external documentation. Methods : We develop a mobile robotic system that enables full-body RGB-D surface scanning. Our work includes a detailed configuration space analysis to identify the environmental parameters that must be considered for a successful surface scan. We validate our findings through an experimental study in the lab and demonstrate the systems application in legal medicine. Results : Our configuration space analysis shows that a good trade-off between coverage and time is reached with three robot base positions, leading to a coverage of 94.96 %. Experiments validate the effectiveness of the system in accurately capturing body surface geometry with an average surface coverage of 96.90±3.16 % and 92.45±1.43 % for a body phantom and actual corpses, respectively. Conclusion : This work demonstrates the potential of a mobile robotic system to automate RGB-D surface scanning in legal medicine, complementing post-mortem CT scans for inner documentation. Our results indicate that the proposed system can contribute to more efficient, autonomous legal medicine documentation, reducing the need for manual intervention.Publicationtype: Journal Article16 10 - 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 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A calibration approach for elasticity estimation with medical tools(De Gruyter, 2024-09-14); ; ; Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-The-Art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature.Publicationtype: Journal Article16 54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , VR-based body tracking to stimulate musculoskeletal training(De Gruyter, 2024-09-14); ; ; ; Training helps to maintain and improve sufficient muscle function, body control, and body coordination. These are important to reduce the risk of fracture incidents caused by falls, especially for the elderly or people recovering from injury. Virtual reality training can offer a cost-effective and individualized training experience. We present an application for the HoloLens 2 to enable musculoskeletal training for elderly and impaired persons to allow for autonomous training and automatic progress evaluation. We designed a virtual downhill skiing scenario that is controlled by body movement to stimulate balance and body control. By adapting the parameters of the ski slope, we can tailor the intensity of the training to individual users. In this work, we evaluate whether the movement data of the HoloLens 2 alone is sufficient to control and predict body movement and joint angles during musculoskeletal training. We record the movements of 10 healthy volunteers with external tracking cameras and track a set of body and joint angles of the participant during training. We estimate correlation coefficients and systematically analyze whether whole body movement can be derived from the movement data of the HoloLens 2. No participant reports movement sickness effects and all were able to quickly interact and control their movement during skiing. Our results show a high correlation between HoloLens 2 movement data and the external tracking of the upper body movement and joint angles of the lower limbsPublicationtype: Journal Article27 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM(Springer, 2024-08-08); ; ; ; Purpose: Commonly employed in polyp segmentation, single-image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages video-based deep learning, harnessing temporal information for superior segmentation performance with least parameter overhead, making it possibly suitable for edge devices. Methods: PolypNextLSTM employs a UNet-like structure with ConvNext-Tiny as its backbone, strategically omitting the last two layers to reduce parameter overhead. Our temporal fusion module, a Convolutional Long Short Term Memory (ConvLSTM), effectively exploits temporal features. Our primary novelty lies in PolypNextLSTM, which stands out as the leanest in parameters and the fastest model, surpassing the performance of five state-of-the-art image and video-based deep learning models. The evaluation of the SUN-SEG dataset spans easy-to-detect and hard-to-detect polyp scenarios, along with videos containing challenging artefacts like fast motion and occlusion. Results: Comparison against 5 image-based and 5 video-based models demonstrates PolypNextLSTM’s superiority, achieving a Dice score of 0.7898 on the hard-to-detect polyp test set, surpassing image-based PraNet (0.7519) and video-based PNS+ (0.7486). Notably, our model excels in videos featuring complex artefacts such as ghosting and occlusion. Conclusion: PolypNextLSTM, integrating pruned ConvNext-Tiny with ConvLSTM for temporal fusion, not only exhibits superior segmentation performance but also maintains the highest frames per speed among evaluated models. Code can be found here: https://github.com/mtec-tuhh/PolypNextLSTM.Publicationtype: Journal Article19 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Needle tracking in low-resolution ultrasound volumes using deep learning(Springer, 2024-07-13); ; ; ; Purpose: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes. Methods: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 × 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation. Results: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning. Conclusion: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.Publicationtype: Journal Article18 82 - 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: without files , Ultrasound Shear Wave Velocity Estimation in a small Field of View via Spatio-Temporal Deep Learning(SPIE, 2023-04-03); ; ; ; A change in tissue stiffness can indicate pathological diseases and therefore supports physicians in diagnosis and treatment. Ultrasound shear wave elastography (US-SWEI) can be used to quantify tissue stiffness by estimating the velocity of propagating shear waves. While a linear US probe with a lateral imaging width of approximately 40 mm is commonly used and US-SWEI has been successfully demonstrated, some clinical applications, such as laparoscopic or endoscopic interventions, require small probes. This limits the lateral image width to the millimeter range and reduces the available information in the US images substantially. In this work, we systematically analyze the effect of a reduced lateral imaging width for shear wave velocity estimation using the conventional time-of-flight (ToF) method and spatio-temporal convolutional neural networks (ST-CNNs). For our study, we use tissue mimicking gelatin phantoms with varying stiffness and resulting shear wave velocities in the range from 3.63 m/s to 7.09 m/s. We find that lateral imaging width has a substantial impact on the performance of ToF, while shear wave velocity estimation with ST-CNNs remains robust. Our results show that shear wave velocity estimation with ST-CNN can even be performed for a lateral imaging width of 2.1 mm resulting in a mean absolute error of 0.81 ± 0.61 m/s.Publicationtype: Conference Paper51