OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis

Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehensive information. However, the limited availability of specific US video cases causes teaching difficulties in identifying corresponding diseases, which potentially impacts the detection rate of such cases. The synthesis of US videos may represent a promising solution to this issue. Nevertheless, it is challenging to accurately animate the intricate motion of dynamic anatomic structures while preserving image fidelity. To address this, we present a novel online feature-decoupling framework called OnUVS for high-fidelity US video synthesis. Our highlights can be summarized by four aspects. First, we introduced anatomic information into keypoint learning through a weakly-supervised training strategy, resulting in improved preservation of anatomical integrity and motion while minimizing the labeling burden. Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator. Third, we adopted a multiple-feature discriminator to extract a comprehensive range of visual cues, thereby enhancing the sharpness and fine details of the generated videos. Fourth, we constrained the motion trajectories of keypoints during online learning to enhance the fluidity of generated videos. Our validation and user studies on in-house echocardiographic and pelvic floor US videos showed that OnUVS synthesizes US videos with high fidelity.Comment: 14 pages, 13 figures and 6 table

Combined transarterial chemoembolization and microwave ablation versus transarterial chemoembolization in BCLC stage B hepatocellular carcinoma

PURPOSE:We aimed to compare the clinical effectiveness of combination therapy of transarterial chemoembolization (TACE) and microwave ablation (MWA) with TACE monotherapy in BCLC stage B HCC patients with tumor size ≤7 cm and tumor number ≤5.METHODS:We retrospectively reviewed 150 BCLC stage B HCC patients who had received TACE monotherapy or TACE-MWA combination therapy in our hospital from March 2007 to April 2016. The patients were matched by propensity score at the ratio of 1:2 by optimal method. The median follow-up period was 16 months. The overall survival, tumor response and progression-free survival were compared between the two groups by Kaplan–Meier method and Log rank test.RESULTS:Tumor response (complete or partial response or stable disease) rates at 6, 12, 18, 24 months were 55.5%, 37.3%, 21.3%, 15.8% for TACE group, and 74%, 47.8%, 35%, 31.8% for TACE-MWA group, respectively. The survival rates at 1, 3, 5 years were 77.5%, 42.1%, 21% for TACE group and 93.1%, 79%, 67.7% for TACE-MWA group, respectively. Compared with TACE group, the TACE-MWA group had significantly improved progression-free survival (P = 0.044) and overall survival (P = 0.002).CONCLUSION:TACE-MWA combination therapy has better clinical effectiveness than TACE monotherapy in BCLC stage B patients with tumor size ≤7 cm and tumor number ≤5

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA approximate to 50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA

Wide-area damping state feedback and output feedback controller design strategy considering time delays and actuator saturation based on parametric Lyapunov theory

Remote signal transmission time delays are the major factors to affect the performance of wide-area damping controller (WADC) and even deteriorate stability of large-scale power system. If the physical structure and the mathematical model of the system are different, the method of mathematically dealing with time delay is not the same. From the perspective of mathematical application engineering, there are two main ways to deal with the communication time delay: linear matrix inequalities (LMI) theory and conventional time-delay predictive compensation theory. Traditional time delay damping controller based on linear matrix inequalities (LMI) suffers relatively large conservatism and complexity. Traditional time delay damping controller based on time delay compensation has no clear relation between the control parameter and the time delay. On the other hand, the saturation of actuator leads to the deterioration of control performance and even threat system stability. The existing methods for saturation are negative feedback anti-saturation after the actuator is saturated, which is an afterwards control and it is difficult to well eliminate the influence of saturation. In this paper, a novel design strategy for wide-area time-delay damping controller based on parametric Lyapunov theory is derived. The explicit expression of the controller parameter value associated with the time delay is determined. This strategy can deal with arbitrary large time-vary delay theoretically and have extremely low controller dimension, which makes it have potential capability to be applied in large-scale inter-connected power systems. The control strategy is determined according to the system stability original definition (The system energy function is finite and positive and its first derivative is negative), which avoids complex matrix inequalities cyclic solution and reduces the conservatism of the controller. Moreover, it can directly give simple and explicit control law and specific control parameters, which enable this method to adjust its parameters online according to the exact time delay obtained by GPS. In the design process, the saturation characteristics of the actuator are taken into account in the linearization model of the closed-loop system. This makes it very adaptable to actuator saturation, so that the proposed design strategy can ensure the small signal stability of the closed-loop system considering time delays and actuator saturation. Case study is undertaken based on a New England ten-machine 39-bus power system, which stands for a large scale power system to verify the feasibility and effectiveness of the proposed design strategy. Compared with the free weight matrix damping controller (FWMC), which is a kind of LMI-based design strategy with excellent performance, the advantages and superiority of the proposed control strategy are verified. The proposed design strategy based on state feedback and output feedback has better control performance than FWMC

Novel Method for Rapidly Constructing Active Power Steady-State Security Regions Incorporating the Equivalent Reactances of TCSCs

Active power steady-state security regions (APSSRs), which can provide guidance for prevention and control through security checks, is of great importance for the safe operation of power systems in which more and more sustainable energy power generation is integrated. As a mature flexible AC transmission system (FACTS) device, thyristor-controlled series compensators (TCSCs) can carry out series compensation for the transmission line by controlling its equivalent reactance. With the change of the equivalent reactance parameter of a TCSC, the nodal admittance matrix and power flow distribution of the power system also changes. Inevitably, the APSSR will be different. Therefore, it is necessary and important to further incorporate the equivalent reactance parameters of TCSCs in the APSSR expression, which is generally established in the space of node active power injections. In this paper, a rapid construction method of APSSRs incorporating the equivalent reactances of TCSCs is proposed. Firstly, applicability and efficiency of the conventional APSSR construction method for power systems with TCSCs are analyzed. Further, with equivalent disconnection of TCSC branches, the effect of TCSC equivalent reactances on the distribution of active power flow through changing the structure parameters is treated as modifying node active power injections. On this basis, explicit expressions of APSSRs with a single TCSC equivalent reactance parameter and double TCSC equivalent reactance parameters are derived, respectively. Moreover, by deducing the general formula of APSSRs with multiple TCSC equivalent reactance parameters, the feasibility of the proposed method for power systems with multiple TCSCs is analyzed. Eventually, via benchmarks with different scales and a different number of TCSCs, validity and superiorities of the proposed method in computational efficiency are demonstrated

Novel Strategy for Accurate Locating of Voltage Sag Sources in Smart Distribution Networks with Inverter-Interfaced Distributed Generators

With the aid of power quality monitoring systems (PQMSs), accurate locating of voltage sag sources, which has important significance for guiding maintenance personnel in finding and repairing faults as well as improving power supply reliability, has been becoming a new research hotspot. However, existing methods have unsatisfactory locating accuracy due to the integration of distributed generators (DGs) and fault resistance. In this paper, a novel strategy for accurately locating voltage sag sources in smart distribution networks is proposed. Based on inverse theory, which is well applied in geophysics, the accurate location issue is treated as a two-step optimization model. It aims at making the distribution of voltage phasors and current phasors obtained by theoretical short-circuit calculation match those actually observed as closely as possible. To guarantee the feasibility of the strategy, the effect of inverter-interfaced DGs (IIDGs) which are the main form of DG is considered in the short-circuit calculations. To guarantee the location accuracy of the strategy, fault resistance is treated as an optimization variable in the two-step optimization model to eliminate estimation error of fault resistance. Via two modified IEEE benchmarks with different scales, the validity and the superiorities in applicability and accuracy of the proposed strategy are verified

Novel Detection Method for Consecutive DC Commutation Failure Based on Daubechies Wavelet with 2nd-Order Vanishing Moments

Accurate detection and effective control strategy of commutation failure (CF) of high voltage direct current (HVDC) are of great significance for keeping the safe and stable operations of the hybrid power grid. At first, a novel detection method for consecutive CF is proposed. Concretely, the 2nd and higher orders’ derivative values of direct current are summarized as the core to judge CF by analyzing the physical characteristics of the direct current waveform of the converter station in CF. Then, the Daubechies wavelet coefficient that can represent the 2nd and higher order derivative values of direct current is derived. Once the wavelet coefficients of the sampling points are detected to exceed the threshold, the occurrence of CF is confirmed. Furthermore, by instantly increasing advanced firing angle β in the inverter side, an additional emergency control strategy to prevent subsequent CF is proposed. Eventually, with simulations of the benchmark model, the effectiveness and superiorities of the proposed detection method and additional control strategy in accuracy and rapidity are verified

Load Restoration Flexible Optimization in Wind Power Integrated System Based on Conditional Value at Risk

In order to better accommodate the uncertainty induced from a high penetration of wind power during load recovery, a method of load restoration flexible optimization is developed in this paper. First, the idea of adopting flexible operational constraints when optimizing schemes is presented so as not to be too conservative in the application of wind power output during restoration. Further, conditional value at risk (CVaR) is employed to define an operational constraint slacking factor (OCSF) through analyzing the extent of constraint exceeding the prescribed limits when wind speed is taking the value outside its confidence interval. Adopting OCSF, a mixed integer linear programming (MILP) model of flexible load restoration, is constructed based on the primary model by substituting adjustable constraints for rigid constraints, which can be solved with CPLEX. Finally, the New England 10-unit 39-bus power system is used to demonstrate the proposed method, and the results explicitly indicate that the amount of load picked up can be effectively increased and an operational security can be guaranteed as well