Ferdinando Rodriguez y Baena

Alex Ranne, Yordanka Velikova, Nassir Navab et al.

To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. Prolonged Fluoroscopic exposure is harmful for the patient and the clinician, and may lead to severe post-operative sequlae such as the development of cancer. Meanwhile, the use of interventional Ultrasound has gained popularity, due to its well-known benefits of small spatial footprint, fast data acquisition, and higher tissue contrast images. However, ultrasound images are hard to interpret, and it is difficult to localise vessels, catheters, and guidewires within them. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. In order to facilitate training of such deep learning networks, we introduce a new data synthesis pipeline that used physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon-based aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound.

Baoru Huang, Tuan Vo, Chayun Kongtongvattana et al.

Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/.

Open-H-Embodiment Consortium, Nigel Nelson, Juo-Tung Chen et al.

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 49 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves 64% average success across a 29-step ex vivo suturing sequence. We also train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation from a single checkpoint, spanning nine robotic platforms and supporting in silico policy evaluation and synthetic data generation for the medical domain. These results suggest that open, large-scale medical robot data collection can serve as critical infrastructure for the research community, enabling advances in robot learning, world modeling, and beyond.

Ivan Tregear, Ayhan Aktas, Ferdinando Rodriguez y Baena

In robotic applications, actuators are typically designed to be stiff with minimal backlash to ensure precision and repeatability. However, this limits compliance, leading to potential damage and poor force control in uncertain environments. Series Elastic Actuation (SEA) introduces compliance to enhance disturbance rejection and enable force measurement via Hooke's Law but reduces system bandwidth. A custom Series Elastic (SE) element was retrofitted to a black-box actuator to mitigate non-linearities like backlash and static friction. Integrating the SE element enabled high-fidelity force measurements, improving force control bandwidth and performance. A torsional SE element was designed through Finite Element (FE) analysis, yielding a stiffness of 2155.4 Nm/rad. Open-loop force control bandwidth was measured for the original motor and the SEA-integrated configuration, while closed-loop bandwidth was assessed using feedback from the SEA and a commercial force sensor. The SEA module increased bandwidth from 10.32 Hz to 30.32 Hz, a 2.93X improvement. Additionally, it outperformed the commercial sensor by 7.63% despite costing 25 GBP, a fraction of the price.

Alex Ranne, Liming Kuang, Yordanka Velikova et al.

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's health due to prolonged radiation exposure. As an alternative, interventional ultrasound has notable benefits such as being radiation-free, fast to deploy, and having a small footprint in the operating room. Yet, ultrasound is hard to interpret, and highly prone to artifacts and noise. Additionally, interventional radiologists must undergo extensive training before they become qualified to diagnose and treat patients effectively, leading to a shortage of staff, and a lack of open-source datasets. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary map estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future.

Connor Daly, Elettra Marconi, Marco Riva et al.

Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-mounted tracking devices, markerless tracking can reduce operating time and complexity. However, its use has been limited to cadaveric studies. This paper introduces the first real-world clinical RGB-D dataset for spine surgery and develops SpineAlign, a system for capturing deformation between preoperative and intraoperative spine states. We also present an intraoperative segmentation network trained on this data and introduce CorrespondNet, a multi-task framework for predicting key regions for registration in both intraoperative and preoperative scenes.

Xue Hu, Anh Nguyen, Ferdinando Rodriguez y Baena

Conventional computer-assisted orthopaedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. Visual tracking has recently been proposed to measure the target anatomy in a markerless and effortless way, but the existing methods fail under real-world occlusion caused by intraoperative interventions. Furthermore, such methods are hardware-specific and not accurate enough for surgical applications. In this paper, we propose a RGB-D sensing-based markerless tracking method that is robust against occlusion. We design a new segmentation network that features dynamic region-of-interest prediction and robust 3D point cloud segmentation. As it is expensive to collect large-scale training data with occlusion instances, we also propose a new method to create synthetic RGB-D images for network training. Experimental results show that our proposed markerless tracking method outperforms recent state-of-the-art approaches by a large margin, especially when an occlusion exists. Furthermore, our method generalises well to new cameras and new target models, including a cadaver, without the need for network retraining. In practice, by using a high-quality commercial RGB-D camera, our proposed visual tracking method achieves an accuracy of 1-2 degress and 2-4 mm on a model knee, which meets the standard for clinical applications.

Claudia D'Ettorre, Andrea Mariani, Agostino Stilli et al.

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.