Morsing Herrera (fifthwrist21)

In addition, the longitudinal displacement node locations are dependent on the resonant frequency of the devices rather than the locations of the piezoceramic elements.We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.The ongoing COVID-19 pandemic, caused by the highly contagious SARS-CoV-2 virus, has overwhelmed healthcare systems worldwide, putting medical professionals at a high risk of getting infected themselves due to a global shortage of personal protective equipment. This has in-turn led to understaffed hospitals unable to handle new patient influx. To help alleviate these problems, we design and develop a contactless patient positioning system that can enable scanning patients in a completely remote and contactless fashion. Our key design objective is to reduce the physical contact time with a patient as much as possible, which we achieve with our contactless workflow. Our system comprises automated calibration, positioning, and multi-view synthesis components that enable patient scan without physical proximity. Our calibration routine ensures system calibration at all times and can be executed without any manual intervention. Our patient positioning routine comprises a novel robust dynamic fusion (RDF) algorithm for accurate 3D patient body modeling. G Protein antagonist With its multi-modal inference capability, RDF can be trained once and used across different applications (without re-training) having various sensor choices, a key feature to enable system deployment at scale. Our multi-view synthesizer ensures multi-view positioning visualization for the technician to verify positioning accuracy prior to initiating the patient scan. We conduct extensive experiments with publicly available and proprietary datasets to demonstrate efficacy. Our system has already been used, and had a positive impact on, hospitals and technicians on the front lines of the COVID-19 pandemic, and we expect to see its use increase substantially globally.This paper presents the use of rendered visual cues as drop shadows and their impact on overall usability and accuracy of grasping interactions for monitor-based exocentric Augmented Reality (AR). We report on two conditions; grasping with drop shadows and without drop shadows and analyse a total of 1620 grasps of two virtual object types (cubes and spheres). We report on the accuracy of one grasp type, the Medium Wrap grasp, against Grasp Aperture (GAp), Grasp Displacement (GDisp), completion time and usability metrics from 30 participants. A comprehensive statistical analysis of the results is presented giving comparisons of the inclusion of drop shadows in AR grasping. Findings showed that the use of drop shadows increases usability of AR grasping while significantly decreasing task completion times. Furthermore, drop shadows also significantly improve user's depth estimation of AR object position. However, this study also shows that using drop shadows does not improve user's