Manual Placement Research Articles

Automated Fiber Embedding for Soft Mechatronic Components

Many recent advances in smart composites for soft robotics have resulted from integration of long fibers to precisely control structural and functional properties. Compared to the time-consuming, laborious and error-prone process of manual long fiber placement typically used, Automated Fiber Embedding (AFE) has an enormous potential in soft robotics due to its repeatability, precision, and ease of integration with commonly used elastomers. In this work, three different approaches for automated layering and embedding of fibers inside silicone elastomers are presented. These techniques enable fabrication of soft composites comprising linear and free-form long fiber patterns without user intervention or manual post processing. To demonstrate the versatility of the techniques, several soft robotic applications ranging from actuators to inductive charging coils are presented.

Nozzleless Acoustic Droplet Ejector With Electrically Tunable Droplet Size for Picking and Placing Semiconductor Chips

This paper presents a microfabricated nozzleless acoustic droplet ejector with electrically tunable droplet size as a new tool for on-demand picking and placing semiconductor chips for semiconductor packaging. The ejector is based on a 2-mm-thick lead zirconate titanate (PZT) sheet with a planar annular-ring air-cavity acoustic Fresnel lens on top. When driven with sinusoidal pulsed voltage signals of 1.16 MHz, the ejector generates focused ultrasound with 1-mm focal diameter and 5-mm focal depth at 22 mm focal length in sodium polytungstate (SPT) solution. A finite-element-method (FEM) simulation model calculating the acoustic-field-induced fluid motion during the droplet ejection process has been developed, and verified by experiments, in which the device ejects SPT droplets whose diameter is from 850 to 2, 490 μm, controlled by the driving pulse width and voltage. The ejected droplets are able to carry 400- μm-thick square silicon chips with side length from 700 to 3, 100 μm. A polyester channel-embedded guiding cover for semi-automatic loading of silicon chips to the ejection site is designed to avoid manual placement of the silicon chips. With the proof-of-concept system, we demonstrate ejecting silicon chips out of SPT surface onto a nearby paper, assembling them into arrays with good repeatability. [2020-0337].

Multicamera 3D Viewpoint Adjustment for Robotic Surgery via Deep Reinforcement Learning

While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [ 1 ], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue–tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [ 2 ], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [ 3 , 4 , 5 ]. The group’s previous work investigated a novel, context-aware autonomous camera positioning method [ 6 ], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [ 6 ] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation.

Optimizing Interstitial Photodynamic Therapy Planning With Reinforcement Learning-Based Diffuser Placement.

To develop new algorithms that generate high-quality plans by optimizing over the light source positions, along with their powers, to minimize the damage to organs-at-risk while eradicating the tumor. The optimization algorithms should also accurately model the physics of light propagation through the use of Monte-Carlo simulators. We use simulated-annealing as a baseline algorithm to place the sources. We propose different source perturbations that are likely to provide better outcomes and study their impact. To minimize the number of moves attempted (and effectively runtime) without degrading result quality, we use a reinforcement learning-based method to decide which perturbation strategy to perform in each iteration. We simulate our algorithm on virtual brain tumors modeling real glioblastoma multiforme cases, assuming a 5-ALA PpIX induced photosensitizer that is activated at[Formula: see text] wavelength. The algorithm generates plans that achieve an average of 46% less damage to organs-as-risk compared to the manual placement used in current clinical studies. Having a general and high-quality planning system makes iPDT more effective and applicable to a wider variety of oncological indications. This paves the way for more clinical trials.

Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography

IntroductionTooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging. MethodsA total of 433 Digital Imaging and Communications in Medicine images of single- and double-rooted teeth randomly selected from 314 anonymized CBCT scans were imported and manually segmented. An AI-driven tooth segmentation algorithm based on a feature pyramid network was developed to automatically detect and segment teeth, replacing manual user contour placement. The AI-driven tool was evaluated based on volume comparison, intersection over union, the Dice score coefficient, morphologic surface deviation, and total segmentation time. ResultsOverall, AI-driven and clinical reference segmentations resulted in very similar segmentation volumes. The mean intersection over union for full-tooth segmentation was 0.87 (±0.03) and 0.88 (±0.03) for semiautomated (SA) (clinical reference) versus fully automated AI-driven (F-AI) and refined AI-driven (R-AI) tooth segmentation, respectively. R-AI and F-AI segmentation showed an average median surface deviation from SA segmentation of 9.96 μm (±59.33 μm) and 7.85 μm (±69.55 μm), respectively. SA segmentations of single- and double-rooted teeth had a mean total time of 6.6 minutes (±76.15 seconds), F-AI segmentation of 0.5 minutes (±8.64 seconds, 12 times faster), and R-AI segmentation of 1.2 minutes (±33.02 seconds, 6 times faster). ConclusionsThis study showed a unique fast and accurate approach for AI-driven automated tooth segmentation on CBCT imaging. These results may open doors for AI-driven applications in surgical and treatment planning in oral health care.

DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems1.

Materials characterization made possible by dual energy CT (DECT) scanners is expected to considerably improve automatic detection of hazardous objects in checked and carry-on luggage at our airports. Training a computer to identify the hazardous items from DECT scans however implies training on a baggage dataset that can represent all the possible ways a threat item can packed inside a bag. Practically, however, generating such data is made challenging by the logistics (and the permissions) related to the handling of the hazardous materials. The objective of this study is to present a software simulation pipeline that eliminates the need for a human to handle dangerous materials and that allows for virtually unlimited variability in the placement of such materials in a bag alongside benign materials. In this paper, we present our DEBISim software pipeline that carries out an end-to-end simulation of a DECT scanner for virtual bags. The key highlights of DEBISim are: (i) A 3D user-interactive graphics editor for constructing a virtual 3D bag with manual placement of different types of objects in it; (ii) An automated virtual bag generation algorithm for creating randomized baggage datasets; (iii) An ability to spawn deformable sheets and liquid-filled containers in a virtual bag to represent plasticized and liquid explosives; and (iv) A GPU-based X-ray forward modelling block for spiral cone-beam scanners used in checked baggage screening. We have tested our simulator using two standard CT phantoms: the American College of Radiology (ACR) phantom and the NIST security screening phantom as well as on a set of reference materials representing commonly encountered items in checked baggage. For these phantoms, we have assessed the quality of the simulator by comparing the simulated data reconstructions with real CT scans of the same phantoms. The comparison shows that the material-specific properties as well as the CT artifacts in the scans generated by DEBISim are close to those produced by an actual scanner. DEBISim is an end-to-end simulation framework for rapidly generating X-ray baggage data for dual energy cone-beam scanners.

Enhancing the handling of standard substitution weights on a hydrostatic weighing apparatus

The hydrostatic weighing apparatus is a first-level method used to measure the density of solids and liquids. At the Portuguese Institute for Quality the hydrostatic tests for the determination of the liquids’ density implies the multiple manual placement of a set of standard substitution weights on apparatus’ balance pan, leading to undesirable perturbations in the weighing environment. Also, by this the eccentricity effects on the balance, contribute for an unwanted increase of the measurement uncertainty. To overcome these phenomena, an automated mechanism was designed and implemented, which allows the placing and recollection of the set of standard weights on the balance. To validate the new mechanism, tests to ultrapure water at 20 °C were executed before and after its implementation and the obtained results were compared.<br />Despite a 3-fold increase in density measurement uncertainty, mainly due to the exposure of the standard substitution weights to air convection currents, the execution of hydrostatic tests was improved, not only for the fact that the measurements are now carried out quicker, but also for the fact that the operator only needs to intervene once during the whole test. Moreover, the smaller absolute deviation from the reference density value for ultrapure water at 20 °C results also in a normalised error <em>E</em><sub>n</sub> lower than 1 (0.3), thus validating the designed automated mechanism for the handling of standard substitution weights.

Static LiDAR Assisted UWB Anchor Nodes Localization

The paper presents an indoor mapping and UWB (ultra-wideband) anchor localization method. The method, unlike most of the solutions described it the literature uses a static LiDAR (Light Detection and Ranging) mounted on a tripod rather than a robotic platform. It can be used at an any place, where employing a robot would be difficult (e.g. private homes), but since it requires manual LiDAR placement it would be most efficient in spaces of moderate areas. The proposed concept consists in mapping the environment of system installation while performing ranging measurements with deployed UWB anchor nodes. The SLAM (Simultaneous Location and Mapping) algorithm used for map integration and device localization relies only on LiDAR results. The matching is performed in two steps by finding an initial match based on corresponding landmarks extracted from the scans (intersections of the detected wall lines) and refining the results using an Iterative Closest Point algorithm. The anchors are localized based on the ranging results and SLAM-derived device locations using a Least-Squares based optimizer. The experiments have shown that the algorithm allows to construct a comprehensive map of the environment and localize the anchors with a root mean square error of 0.34 m, which is at similar level to analogous methods described in other works. The impact of anchor localization error on the systems performance was not significant. In both static and dynamic scenarios the difference in median errors obtained using reference and mapped anchors’ locations was about 0.05 m.

Design and implementation of a hybrid DPWM under 50 ps resolution based on general‐purpose FPGA

SummaryHigh‐resolution pulse width modulators are used widely in different fields of electrical engineering, such as dimming of light‐emitting diode (LED) lighting, motor control, RF modulators, audio amplifiers, and switch‐mode power supplies. To realize a high‐resolution digital pulse‐width modulator (DPWM) in a limited inner system clock, a simple implementation of a hybrid DPWM with the resolution under 50 ps based on a general‐purpose field‐programmable gate array (FPGA) is described. The multiplexer device implementing the fast carry‐chain path and an AND gate controlling the selection input are used as a delay unit. The manual routing or placement is not required in the proposed approach, which just needs some conditional constraints. Some different conditional constraints influencing the monotonicity and resolution of DPWM are discussed. Finally, a 1 MHz switching frequency DPWM with 40 ps resolution is experimentally demonstrated, with high monotonicity and linearity. Further, a synchronous buck with and without this high‐resolution DPWM is experimentally compared to illustrate the regulation resolution.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Unmanned Aerial Sensor Placement for Cluttered Environments

Unmanned aerial vehicles (UAVs) have been shown to be useful for the installation of wireless sensor networks (WSNs). More notably, the accurate placement of sensor nodes using UAVs, opens opportunities for many industrial and scientific uses, in particular, in hazardous environments or inaccessible locations. This publication proposes and demonstrates a new aerial sensor placement method based on impulsive launching. Since direct physical interaction is not required, sensor deployment can be achieved in cluttered environments where the target location cannot be safely approached by the UAV, such as under the forest canopy. The proposed method is based on mechanical energy storage and an ultralight shape memory alloy (SMA) trigger. The developed aerial system weighs a total of 650 grams and can execute up to 17 deployments on a single battery charge. The system deploys sensors of 30 grams up to 4 meters from a target with an accuracy of ±10 cm. The aerial deployment method is validated through more than 80 successful deployments in indoor and outdoor environments. The proposed approach can be integrated in field operations and complement other robotic or manual sensor placement procedures. This would bring benefits for demanding industrial applications, scientific field work, smart cities and hazardous environments [Video attachment: https://youtu.be/duPRXCyo6cY].

P15. Power-assisted pedicle screw placement decreases screw wobble

P15. Power-assisted pedicle screw placement decreases screw wobble

An automated method for defining anatomic coordinate systems in the hindfoot

The absence of a standardized method for defining hindfoot bone coordinate systems makes it difficult to compare kinematics results from different research studies. The purpose of this study was to develop a reliable and robust procedure for defining anatomical coordinate systems for the talus and calcaneus. Four methods were evaluated based upon their anatomic consistency across subjects, repeatability, and their correspondence to functional axes of rotation. The four systems consisted of: 1) interactively identified bony landmarks, 2) a principal component analysis, 3) automatically identified bony landmarks, and 4) translating the tibial coordinate system to the hindfoot bones. The four systems were evaluated on 40 tali and 40 calcanei. The functional axes of rotation were determined using dynamic biplane radiography to image the hindfoot during gait. Systems 2 and 3 were the most repeatable and consistent due to the lack of operator intervention when defining coordinate systems. None of the coordinate systems corresponded well to functional axes of rotation during gait. System 3 is recommended over System 2 because it more closely mimics established bone angles measured clinically, especially for the calcaneus. This study presents an automated method for defining anatomic coordinate systems in the talus and calcaneus that does not rely on manual placement of markers or fitting of spheres to the bone surfaces which are less reliable due to operator-dependent measurements. Using this automated method will make it easier to compare hindfoot kinematics results across research studies.

Fully Automated Pipeline for Body Composition Estimation from 3D Optical Scans using Principal Component Analysis: A Shape Up Study.

The increasing prevalence and adaptability of 3D optical scan (3DO) technology has invoked many recent studies which use 3DO scanning as a convenient and inexpensive means for predicting body composition and health risks. The Shape Up studies seek a device-agnostic solution for body composition estimation based on principal component analysis (PCA). This paper reports a progress made on Shape Up's previous work which served as a criterion analysis for PCA-based body composition and health risk prediction. This study presents proof-of-concept for a novel automated landmark detection step that allows for a fully automated PCA-based approach to body composition estimation that facilitates a practical device-agnostic PCA-based solution to body composition estimation from 3DO scans. Our results show that replacing expensive and time-consuming manual point placement with the proposed automated landmarks will not diminish the quality of body composition estimates allowing for a more practical pipeline that can be used in real-world settings.

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Accuracy of manual and automatic placement of an anatomical coordinate system for the full or partial radius in 3D space

Accurate placement of a coordinate system on the radius is important to quantitatively report 3D surgical planning parameters or joint kinematics using 4D imaging techniques. In clinical practice, the scanned length of the radial shaft varies among scanning protocols and scientific studies. The error in positioning a radial coordinate system using a partially scanned radius is unknown. This study investigates whether the imaged length of the radius significantly affects the positioning of the coordinate system. For different lengths of the radius, the error of positioning a coordinate system was determined when placed automatically or manually. A total of 85 healthy radii were systematically shortened until 10% of the distal radius remained. Coordinate systems were placed automatically and manually at each shortening step. A linear mixed model was used to associate the positioning error with the length of the radial shaft. The accuracy and precision of radial coordinate system placement were compared between automatic and manual placement. For automatic placement of the radial coordinate system, an increasing positioning error was associated with an increased shortening of the radius (P = < 0.001). Automatic placement is superior to manual placement; however, if less than 20% of the radial shaft length remains, manual placement is more accurate.

Preliminary clinical application of the robot-assisted CT-guided irreversible electroporation ablation for the treatment of pancreatic head carcinoma.

To evaluate the feasibility and safety of a robot-guided irreversible electroporation (IRE) ablation system for the treatment of pancreatic head carcinoma. A total of 20 cases with pancreatic head carcinoma were divided into two groups: 11 cases in group A with manual probe placement and 9 cases in group B with robotic navigated probe placement. The two groups were compared in terms of planning time before puncture, puncture time, the total time of electrode deployment, number of scans, and punctual accuracy of the single electrode. Each probe was successfully punctured, and no complications were detected. P-values were calculated for all the parameters, using the SPSS 25.0 software and the t test. The new robot can reduce the total operating time as compared to the manual probe placement with the same accuracy in the IRE of pancreatic head carcinoma.

Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer.

In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.

Automatic landmarking as a convenient prerequisite for geometric morphometrics. Validation on cone beam computed tomography (CBCT)- based shape analysis of the nasal complex

Manual landmarking is used in several manual and semi-automated prediction guidelines for approximation of the nose. The manual placement of landmarks may, however, render the analysis less repeatable due to observer subjectivity and, consequently, have an impact on the accuracy of the human facial approximation. In order to address this subjectivity and thereby improve facial approximations, we are developing an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using non-rigid surface registration. The aim of this study was to validate the automatic landmarking method by comparing the intra-observer errors (INTRA-OE) and inter-observer errors (INTER-OE) between automatic and manual landmarking.Cone beam computed tomography (CBCT) scans of adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. In this study, the validation of the automatic landmarking was performed on 20 3D surfaces. INTRA-OE and INTER-OE were analyzed by registering 41 craniometric landmarks from 10 hard-tissue surfaces and 21 capulometric landmarks from 10 soft-tissue surfaces of the same individuals. Absolute precision of the landmark positioning (both on the samples as well as the template) was assessed by calculating the measurement error (ME) for each landmark over different observers. Systematic error (bias) and relative random error (precision) was further quantified through repeated measures ANOVA (ANOVA-RM).The analysis showed that the random component of the ME in landmark positioning between the automatic observations were on average on par with the manual observations, except for the soft-tissue landmarks where automatic landmarking showed lower ME compared to manual landmarking. No bias was observed within the craniometric landmarking methods, but some bias was observed for capulometric landmarking.In conclusion, this research provides a first validation of the precision and accuracy of the automatic placement of landmarks on 3D hard- and soft-tissue surfaces and demonstrates its utilization as a convenient prerequisite for geometric morphometrics based shape analysis of the nasal complex.

Operation of the Collaborative Composite Manufacturing (CCM) System.

The automated tape placement and the automated fiber placement (AFP) machines provide a safer working environment and reduce the labor intensity of workers than the traditional manual fiber placement does. Thus, the production accuracy, repeatability and efficiency of composite manufacturing are significantly improved. However, the current AFP systems can only produce the composite components with large open surface or simple revolution parts, which cannot meet the growing interest in small complex or closed structures from industry. In this research, by employing a 1-degree of freedom (DoF) rotational stage, a 6-RSS parallel robot, and a 6-DoF serial robot, the dexterity of the AFP system can be significantly improved for manufacturing complex composite parts. The rotational stage mounted on the parallel robot is utilized to hold the mandrel and the serial robot carries the placement head to mimic two human hands that have enough dexterity to lay the fiber to the mandrel with complex contour. Although the CCM system increases the flexibility of composite manufacturing, it is quite time-consuming or even impossible to generate the feasible off-line path, which ensures uniform lay-up of subsequent fibers considering the constraints like singularities, collisions between the fiber placement head and mandrel, smooth fiber direction change and keeping the fiber placement head along the norm of the part's surface, etc. Moreover, due to the existing positioning error of the robots, the on-line path correction is needed. Therefore, the on-line pose correction algorithm is proposed to correct the paths of both parallel and serial robots, and to keep the relative path between the two robots unchanged through the visual feedback when the constraint or singularity problems in the off-line path planning occur. The experimental results demonstrate the designed CCM system can fulfill the movement needed for manufacturing a composite structure with Y-shape.