Greenhooper3344

We perform extensive quantitative and qualitative experiments on four challenging public benchmarks, i.e., DAVIS16, DAVIS17, FBMS and YouTube-Objects. Results show that our method achieves compelling performance against current state-of-the-art ZVOS methods. To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task dynamic visual attention prediction (DVAP). The experiments on two popular datasets (i.e., Hollywood-2 and UCF-Sports) further verify the superiority of our model. Our implementations have been made publicly available at https//github.com/tfzhou/MATNet.Thisstudy focuses on evaluating the real-time functionality of a customized interface and investigating the optimal parameters for intracardiac subharmonic-aided pressure estimation (SHAPE) utilizing Definity (Lantheus Medical Imaging Inc., North Billerica, MA, USA) and Sonazoid (GE Healthcare, Oslo, Norway) microbubbles. Pressure measurements within the chambers of the heart yield critical information for managing cardiovascular diseases. An alternative to current, invasive, clinical cardiac catheterization procedures is utilizing ultrasound contrast agents and SHAPE to noninvasively estimate intracardiac pressures. Therefore, this work developed a customized interface (on a SonixTablet, BK Ultrasound, Peabody, MA, USA) for real-time intracardiac SHAPE. In vitro, a Doppler flow phantom was utilized to mimic the dynamic pressure changes within the heart. Definity (15.0- [Formula see text] microspheres corresponding to 0.1-0.15 mL) and Sonazoid (GE Healthcare; 0.4- [Formula see text] microspheres corresponding to 0.05-0.15 mL) microbubbles were used. Data were acquired for varying transmit frequencies (2.5-4.0 MHz), and pulse shaping options (square wave and chirp down) to determine optimal transmit parameters. Simultaneously obtained radio frequency data and ambient pressure data were compared. For Definity, the chirp down pulse at 3.0 MHz yielded the highest correlation ( r = - 0.77 ± 0.2 ) between SHAPE and pressure catheter data. For Sonazoid, the square wave pulse at 2.5 MHz yielded the highest correlation ( r = - 0.72 ± 0.2 ). In conclusion, the real-time functionality of the customized interface has been verified, and the optimal parameters for utilizing Definity and Sonazoid for intracardiac SHAPE have been determined.In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artifacts. Despite being nonconvex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artifacts in LUS images. To reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method, and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients.Pulsed laser diodes (PLDs) promise to be an attractive alternative to solid-state laser sources in photoacoustic tomography (PAT) due to their portability, high-pulse repetition frequency (PRF), and cost effectiveness. However, due to their lower energy per pulse, which, in turn, results in lower fluence required per photoacoustic signal generation, PLD-based photoacoustic systems generally have maximum imaging depth that is lower in comparison to solid-state lasers. Averaging of multiple frames is usually employed as a common practice in high PRF PLD systems to improve the signal-to-noise ratio of the PAT images. In this work, we demonstrate that by combining the recently described approach of subpitch translation on the receive-side ultrasound transducer alongside averaging of multiple frames, it is feasible to increase the depth sensitivity in a PLD-based PAT imaging system. Here, experiments on phantom containing diluted India ink targets were performed at two different laser energy level settings, that is, 21 and [Formula see text]. Results obtained showed that the imaging depth improves by ~38.5% from 9.1 to 12.6 mm for 21- [Formula see text] energy level setting and by ~33.3% from 10.8 to 14.4 mm for 27- [Formula see text] energy level setting by using λ /4-pitch translation and average of 128 frames in comparison to λ -pitch data acquired with the average of 128 frames. However, the achievable frame rate is reduced by a factor of 2 and 4 for λ /2 and λ /4 subpitch translation, respectively.Domain adaptation has great values in unpaired cross-modality image segmentation, where the training images with gold standard segmentation are not available from the target image domain. The aim is to reduce the distribution discrepancy between the source and target domains. Hence, an effective measurement for this discrepancy is critical. In this work, we propose a new metric based on characteristic functions of distributions. This metric, referred to as CF distance, enables explicit domain adaptation, in contrast to the implicit manners minimizing domain discrepancy via adversarial training. this website Based on this CF distance, we propose an unsupervised domain adaptation framework for cross-modality cardiac segmentation, which consists of image reconstruction and prior distribution matching. We validated the method on two tasks, i.e., the CT-MR cross-modality segmentation and the multi-sequence cardiac MR segmentation. Results showed that the proposed explicit metric was effective in domain adaptation, and the segmentation method delivered promising and superior performance, compared to other state-of-the-art techniques. The data and source code of this work has been released via https//zmiclab.github.io/projects.html.