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Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines under large rotations, or encode local geometry that is less distinctive. To address this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse local regions, rotation-invariant local geometry is encoded into geometric descriptors. Global awareness of 3D structures and geometric context is subsequently incorporated, both in a rotation-invariant fashion. More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions. Geometric context from the whole scene is then globally aggregated into descriptors. Finally, the description of sparse regions is interpolated to dense point descriptors, from which correspondences are extracted for registration. To validate our approach, we conduct extensive experiments on both object- and scene-level data. With large rotations, RIGA surpasses the state-of-the-art methods by a margin of 8 ° in terms of the Relative Rotation Error on ModelNet40 and improves the Feature Matching Recall by at least 5 percentage points on 3DLoMatch.

Purpose: Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow recognition show potential in increasing the quality of patient care, the quantity of video data has surpassed the scale at which images can be manually anonymized. Existing automated 2D anonymization methods under-perform in Operating Rooms (OR), due to occlusions and obstructions. We propose to anonymize multi-view OR recordings using 3D data from multiple camera streams., Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene. We then detect each individual's face in 3D by regressing a parametric human mesh model onto detected 3D human keypoints and aligning the face mesh with the fused 3D point cloud. The mesh model is rendered into every acquired camera view, replacing each individual's face., Results: Our method shows promise in locating faces at a higher rate than existing approaches. DisguisOR produces geometrically consistent anonymizations for each camera view, enabling more realistic anonymization that is less detrimental to downstream tasks., Conclusion: Frequent obstructions and crowding in operating rooms leaves significant room for improvement for off-the-shelf anonymization methods. DisguisOR addresses privacy on a scene level and has the potential to facilitate further research in SDS., (© 2023. The Author(s).)

The field of robotic microsurgery and micro-manipulation has undergone a profound evolution in recent years, particularly with regard to the accuracy, precision, versatility, and dexterity. These advancements have the potential to revolutionize high-precision biomedical procedures, such as neurosurgery, vitreoretinal surgery, and cell micro-manipulation. However, a critical challenge in developing micron-precision robotic systems is accurately verifying the end-effector motion in 3D. Such verification is complicated due to environmental vibrations, inaccuracy of mechanical assembly, and other physical uncertainties. To overcome these challenges, this paper proposes a novel single-camera framework that utilizes mirrors with known geometric parameters to estimate the 3D position of the microsurgical instrument. Euclidean distance between reconstructed points by the algorithm and the robot movement recorded by the highly accurate encoders is considered an error. Our method exhibits an accurate estimation with the mean absolute error of 0.044 mm when tested on a 23G surgical cannula with a diameter of 0.640 mm and operates at a resolution of 4024 × 3036 at 30 frames per second.

Purpose: Mutual acceptance is required for any human-to-human interaction. Therefore, one would assume that this also holds for robot-patient interactions. However, the medical robotic imaging field lacks research in the area of acceptance. This work, therefore, aims at analyzing the influence of robot-patient interactions on acceptance in an exemplary medical robotic imaging system., Methods: We designed an interactive human-robot scenario, including auditive and gestural cues, and compared this pipeline to a non-interactive scenario. Both scenarios were evaluated through a questionnaire to measure acceptance. Heart rate monitoring was also used to measure stress. The impact of the interaction was quantified in the use case of robotic ultrasound scanning of the neck., Results: We conducted the first user study on patient acceptance of robotic ultrasound. Results show that verbal interactions impacts trust more than gestural ones. Furthermore, through interaction, the robot is perceived to be friendlier. The heart rate data indicates that robot-patient interaction could reduce stress., Conclusions: Robot-patient interactions are crucial for improving acceptance in medical robotic imaging systems. While verbal interaction is most important, the preferred interaction type and content are participant dependent. Heart rate values indicate that such interactions can also reduce stress. Overall, this initial work showed that interactions improve patient acceptance in medical robotic imaging, and other medical robot-patient systems can benefit from the design proposals to enhance acceptance in interactive scenarios., (© 2023. The Author(s).)

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans , a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems.

Childhood obesity has become a major focus of public health and subject to increased news coverage. News can shape public understanding of childhood obesity by selective reporting (framing) which can affect policy support and weight stigma. As news is consumed on social media, comments on articles present a novel method to explore public understanding. This study examined how childhood obesity is framed by news articles on Facebook and how individuals commenting understand and react to these articles. This study used a qualitative research design. Facebook pages of 11 national UK news outlets were searched for news articles on childhood obesity published between May 2015 and May 2020. Of those, 30 articles were randomly selected. Framing analysis was used to determine whether childhood obesity was portrayed as a behavioral, societal or medical issue. Responding comments ( N = 1,104) were grouped according to the dominant frame of the corresponding article and analyzed using qualitative content analysis. Of the 30 articles, 28 mentioned societal, 26 behavioral and 18 medical aspects of childhood obesity, highlighting that most articles included more than one frame. Three themes were generated from responding comments: Culprits and Remedies, Appraising Childhood Obesity and Making Sense of the News Article . Findings showed that comments related to Appraising Childhood Obesity differed between differently framed articles, while the other themes did not. This study highlights the need for improved communication on childhood obesity to address weight stigma and improve understanding of news articles.

Background: The COVID-19 pandemic led to the UK government enforcing lockdown restrictions to control virus transmission. Such restrictions present opportunities and barriers for physical activity and healthy eating. Emerging research suggests that in the early stages of the pandemic, physical activity levels decreased, consumption of unhealthy foods increased, while levels of mental distress increased. Our aims were to understand patterns of diet, physical activity, and mental health during the first lockdown, how these had changed twelve-months later, and the factors associated with change., Methods: An online survey was conducted with UK adults (N = 636; 78% female) during the first national lockdown (May-June 2020). The survey collected information on demographics, physical activity, diet, mental health, and how participants perceived lifestyle behaviours had changed from before the pandemic. Participants who provided contact details were invited to complete a twelve-month follow-up survey (May-June 2021), 160 adults completed the survey at both time-points. Descriptive statistics, T-tests and McNemar Chi Square statistics were used to assess patterns of diet, physical activity, and mental health at baseline and change in behaviours between baseline and follow-up. Linear regression models were conducted to explore prospective associations between demographic and psycho-social variables at baseline with change in healthy eating habit, anxiety, and wellbeing respectively., Results: Between baseline and follow-up, healthy eating habit strength, and the importance of and confidence in eating healthily reduced. Self-rated health (positively) and confidence in eating healthily (negatively) were associated with change in healthy eating habit. There were no differences between baseline and follow-up for depression or physical activity. Mean anxiety score reduced, and wellbeing increased, from baseline to follow-up. Living with children aged 12-17 (compared to living alone) was associated with an increase in anxiety, while perceiving mental health to have worsened during the first lockdown (compared to staying the same) was associated with reduced anxiety and an increase in mental wellbeing., Conclusions: While healthy eating habits worsened in the 12 months since the onset of the pandemic, anxiety and mental wellbeing improved. However, anxiety may have increased for parents of secondary school aged children., (© 2022. The Author(s).)

Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently under development to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and insertion of the instrument into the eye represents an additional cognitive effort, and is therefore one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of the robotic setup prior to the surgical task and therefore, increase the intuitiveness of the system integration into clinical workflow.

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from O(N 4 ) to O(N 3 ) (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.

Purpose: The staging of female breast cancer requires detailed information about the level of cancer spread through the lymphatic system. Common practice to obtain this information for patients with early-stage cancer is sentinel lymph node (SLN) biopsy, where LNs are radioactively identified for surgical removal and subsequent histological analysis. Punch needle biopsy is a less invasive approach but suffers from the lack of combined anatomical and nuclear information. We present and evaluate a system that introduces live collaborative robotic 2D gamma imaging in addition to live 2D ultrasound to identify SLNs in the surrounding anatomy., Methods: The system consists of a robotic arm equipped with both a gamma camera and a stereoscopic tracking system that monitors the position of an ultrasound probe operated by the physician. The arm cooperatively places the gamma camera parallel to the ultrasound imaging plane to provide live multimodal visualization and guidance. We validate the system by evaluating the target registration errors between fused nuclear and US image data in a phantom consisting of two spheres, one of which is filled with radioactivity. Medical experts perform punch biopsies on agar-gelatine phantoms with complex configurations of hot and cold lesions to provide a qualitative and quantitative evaluation of the system., Results: The average point registration error for the overlay is [Formula: see text] mm. The time of the entire procedure was reduced by 36 %, with 80v of the biopsies being successful. The users' feedback was very positive, and the system was deemed to be very intuitive, with handling similar to classic US-guided needle biopsy., Conclusion: We present and evaluate the first medical collaborative robotic imaging system. Feedback from potential users for SLN punch needle biopsy is encouraging. Ongoing work investigates the clinical feasibility with more complex and realistic phantoms.