45 results
Filters
Settings
Now showing1 - 10 of 45
- 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 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning-based target localization in robotic radioguided surgery in noisy environments(De Gruyter, 2025-12-01); ; ; ; Rojas, M.In radioguided surgery, G-probes are used intraoperatively to localize targets marked by radionuclides. However, the interpretation of G-probe measurements is challenging due to background activity from surrounding organs. This work investigated whether deep neural networks can localize radiation sources in such environments and how different background activities impact this task. A physics-guided forward model simulated G-probe measurements for different intra-abdominal distributions, including an anatomically inspired bladder activity. A convolutional neural network was trained on simulated measurements to predict 3D target source positions. Results indicated that prediction accuracy improved with more Gprobe measurements and degraded with increased background activity. In particular, proximity to high-activity regions like the bladder significantly reduced accuracy. This study demonstrates the need to consider background activity distributions for target localization and that a convolutional neural network could solve this task.Publicationtype: Journal Article15 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 Article9 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Tracking any point methods for markerless 3D tissue tracking in endoscopic stereo images(De Gruyter, 2025-12-01); ; ; ; Minimally invasive surgery presents challenges such as dynamic tissue motion and a limited field of view. Accurate tissue tracking has the potential to support surgical guidance, improve safety by helping avoid damage to sensitive structures, and enable context-aware robotic assistance during complex procedures. In this work, we propose a novel method for markerless 3D tissue tracking by leveraging 2D Tracking Any Point (TAP) networks. Our method combines two CoTracker models, one for temporal tracking and one for stereo matching, to estimate 3D motion from stereo endoscopic images. We evaluate the system using a clinical laparoscopic setup and a robotic arm simulating tissue motion, with experiments conducted on a synthetic 3D-printed phantom and a chicken tissue phantom. Tracking on the chicken tissue phantom yielded more reliable results, with Euclidean distance errors as low as 1.1 mm at a velocity of 10 mm/s. These findings highlight the potential of TAP-based models for accurate, markerless 3D tracking in challenging surgical scenarios.Publicationtype: Journal Article25 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Portable gait-asymmetry detection using lowcost hardware and machine learningGait analysis provides insights into human motion by examining how individuals walk. However, the high cost associated with gait centers prevents conducting gait analysis regularly, reducing opportunities for early detection and prevention of gait-related issues before pain or injuries occur. The approach presented in this paper integrates low-cost yet computationally powerful hardware with signal processing and machine learning to develop a wearable sensor node placed at the pelvis that continuously collects gait data, providing personalized gait analysis. By positioning the wearable at the pelvis, gait asymmetries can be captured accurately. The approach is validated in a laboratory experiment with 15 participants walking on a treadmill and verified in a free-moving environment. Results indicate that the wearable detects gait asymmetries effectively, enhancing applicability in both clinical and non-clinical settings, supporting rehabilitation and preventive care in a cost-efficient manner.Publicationtype: Journal Article41 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Enhancing Wrist Telerehabilitation: Integrating Haptic Feedback and Remote Evaluation Models(De Gruyter, 2024-12-19); ; Stroke patients with hemiparesis often face significant challenges in accessing consistent and effective rehabilitation therapy due to limitations in mobility and the availability of specialized care. To address this, anew telerehabilitation system is introduced for stroke patients with hemiparesis. It consists of two internet-connected robots for remote wrist rehabilitation. A cloud-based website serves as a centralized interface for patients and doctors, allowing remote control of the system during therapy sessions. To enhance the haptic feedback and stability of the system, a torque/velocity control algorithm isimplemented. These control algorithms incorporate damping and spring terms from an impedance control algorithm to synchronize and maintain the positions of both end effectors. Additionally, a wrist evaluation model has been introduced to calculate stiffness and damping coefficients, essential for monitoring patient progress and improving treatment efficacy. Thewrist model is evaluated using multiple linear regressionmethod. Experimental testing demonstrates the potential of the proposed approach in enhancing telerehabilitation system performance.Publicationtype: Journal Article32 38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Empirical study of magnet distance on magneto-mechanical resonance frequency(De Gruyter, 2024-12-19); ; ; ; Determining the position and orientation of amedical instrument is essential for accurate procedures in en-doscopy, surgery, and vascular interventions. Recently, a novelsensor based on torsional pendulum-like magneto-mechanicalmotion has been proposed. This sensor is passive, wireless andinductively coupled to a transmit-receive coil array. This setupallows the determination of all 6 degrees of freedom usingthe characteristic resonance of the sensor. Additional physicalquantities such as temperature and pressure can be measuredbased on the frequency of the sensor, which mainly dependson the distance between the two involved permanent magnets.In this study, we analyze a sensor composed of two magneticcylinders with variable magnet-to-magnet distance and a basicphysical model based on a dipole assumption. Experimentalanalysis of the resonance frequency and comparison with themodel values show both qualitative and quantitative agreementwith an average relative error of only 0.8 %. This validates theimplemented model and shows the suitability of our magnetic-mechanical resonator made from cylindrical permanent mag-nets for sensing applications.Publicationtype: Journal Article24 69 - 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 , 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 Article14 54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , 6-degree vision based tracking of a mandible phantom with deep learning(De Gruyter, 2024-09-12); ; ; During maxillofacial surgery, the precise placement of surgical tools is crucial for accurate implant placement. Particularly if multiple implants are needed, e.g., after cancerous bone removal, visual landmarks might not be obtainable. To this end, we propose a vision-based tracking approach with deep learning. Our markerless tracking approach is based on video streams from two cameras for tracking a mandible phantom. We study, to what extent vision-based localization using deep learning is feasible. For real-time 6D pose estimation we propose a Siamese network with a ResNet-18 subnetwork. We acquire a large training dataset with a robot and evaluate the tracking accuracy on partially occluded images. Thereby, we mimic visual information that is accessible during clinical interventions. We report a mean position error of 1.33 ± 1.14mm and a rotation error of 0.86 ± 0.71 deg for partially occluded images. Overall we present a promising tracking approach that is marker-free and robust toward image artifacts.Publicationtype: Journal Article28 64