Cooperative Robotic Gamma Imaging: Enhancing US-guided Needle Biopsy

Esposito, Marco; Busam, Benjamin; Hennersperger, Christoph; Rackerseder, Julia; Lu, An; Navab, Nassir; Frisch, B.

doi:10.1007/978-3-319-24571-3_73

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…While using a robotic arm eliminates these disadvantages, it requires developments to ensure the required safety for patient applications. Collaborative medical robotic systems that interact with a human operator in order to provide nuclear imaging in addition to ultrasound, 28 or that can autonomously acquire ultrasound images on human probands 29 are a precursor for the wider introduction of robotic imaging. Additionally, a robot enables the incorporation of optimized trajectories based on the scanning geometry 30 to even further reduce acquisition times.…”

Section: Discussionmentioning

confidence: 99%

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode

et al. 2016

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Purpose: Hand-and robot-guided mini gamma cameras have been introduced for the acquisition of single-photon emission computed tomography (SPECT) images. Less cumbersome than whole-body scanners, they allow for a fast acquisition of the radioactivity distribution, for example, to differentiate cancerous from hormonally hyperactive lesions inside the thyroid. This work compares acquisition protocols and reconstruction algorithms in an attempt to identify the most suitable approach for fast acquisition and efficient image reconstruction, suitable for localization of extended sources, such as lesions inside the thyroid. Methods: Our setup consists of a mini gamma camera with precise tracking information provided by a robotic arm, which also provides reproducible positioning for our experiments. Based on a realistic phantom of the thyroid including hot and cold nodules as well as background radioactivity, the authors compare "step and shoot" (SAS) and continuous data (CD) acquisition protocols in combination with two different statistical reconstruction methods: maximum-likelihood expectation-maximization (ML-EM) for time-integrated count values and list-mode expectation-maximization (LM-EM) for individually detected gamma rays. In addition, the authors simulate lower uptake values by statistically subsampling the experimental data in order to study the behavior of their approach without changing other aspects of the acquired data. Results: All compared methods yield suitable results, resolving the hot nodules and the cold nodule from the background. However, the CD acquisition is twice as fast as the SAS acquisition, while yielding better coverage of the thyroid phantom, resulting in qualitatively more accurate reconstructions of the isthmus between the lobes. For CD acquisitions, the LM-EM reconstruction method is preferable, as it yields comparable image quality to ML-EM at significantly higher speeds, on average by an order of magnitude. Conclusions: This work identifies CD acquisition protocols combined with LM

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Section: Discussionmentioning

confidence: 99%

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode

et al. 2016

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“…Typical augmented reality applications are driven by markers such as AR-Tag [19], ArUcO [23], ARToolkit [34] or AprilTag [54]. These are also used for sensor fusion [17] and extended to high accuracy systems [4]. Reliable and robust detection is of particular interest in the medical domain [18], where selfadhesive markers allow flexible usage [6].…”

Section: Related Workmentioning

confidence: 99%

“…The motives for this can be different: One may want to extract scene content for accurate measurements [4], camera localization [52] or 3D reconstruction [40]. Another driver can be geometric image manipulation [29,8] or sensor fusion [17]. Also human-robot interaction [6] Fig.…”

Section: Introductionmentioning

confidence: 99%

I Like to Move It: 6D Pose Estimation as an Action Decision Process

Busam,

Jung,

Navab

2020

Preprint

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Object pose estimation is an integral part of robot vision and augmented reality. Robust and accurate pose prediction of both object rotation and translation is a crucial element to enable precise and safe human-machine interactions and to allow visualization in mixed reality. Previous 6D pose estimation methods treat the problem either as a regression task or discretize the pose space to classify. We reformulate the problem as an action decision process where an initial pose is updated in incremental discrete steps that sequentially move a virtual 3D rendering towards the correct solution. A neural network estimates likely moves from a single RGB image iteratively and determines so an acceptable final pose. In comparison to previous approaches that learn an objectspecific pose embedding, a decision process allows for a lightweight architecture while it naturally generalizes to unseen objects. Moreover, the coherent action for process termination enables dynamic reduction of the computation cost if there are insignificant changes in a video sequence. While other methods only provide a static inference time, we can thereby automatically increase the runtime depending on the object motion. We evaluate robustness and accuracy of our action decision network on video scenes with known and unknown objects and show how this can improve the state-of-the-art on YCB videos [81] significantly.

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“…Robotic manipulators are already an integral part in modern workplaces where they are often used for repetitive tasks [2]. While humanrobot collaboration can even help in medical applications [3], [4], it is often restricted to cases with clearly defined a priori motion patterns. When the interaction is more intricate and active manipulation is required, a priori definition becomes non-trivial such as in a less structured domestic environments where service robots typically operate.…”

Section: Introductionmentioning

confidence: 99%

DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration

Wang¹,

Manhardt²,

Minciullo³

et al. 2021

Preprint

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The ability to successfully grasp objects is crucial in robotics, as it enables several interactive downstream applications. To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. While the former approaches do not scale well to multiple object instances or classes yet, the latter require large annotated datasets and are hampered by their poor generalization capabilities to new geometries. To overcome these shortcomings, we propose to teach a robot how to grasp an object with a simple and short human demonstration. Hence, our approach neither requires many annotated images nor is it restricted to a specific geometry. We first present a small sequence of RGB-D images displaying a human-object interaction. This sequence is then leveraged to build associated hand and object meshes that represent the depicted interaction. Subsequently, we complete missing parts of the reconstructed object shape and estimate the relative transformation between the reconstruction and the visible object in the scene. Finally, we transfer the a-priori knowledge from the relative pose between object and human hand with the estimate of the current object pose in the scene into necessary grasping instructions for the robot. Exhaustive evaluations with Toyota's Human Support Robot (HSR) in real and synthetic environments demonstrate the applicability of our proposed methodology and its advantage in comparison to previous approaches.

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Cooperative Robotic Gamma Imaging: Enhancing US-guided Needle Biopsy

Cited by 15 publications

References 11 publications

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode

I Like to Move It: 6D Pose Estimation as an Action Decision Process

DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration

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