Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-label. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.Comment: 8 pages,10 figure

Atomic Sn–enabled high-utilization, large-capacity, and long-life Na anode

Constructing robust nucleation sites with an ultrafine size in a confined environment is essential toward simultaneously achieving superior utilization, high capacity, and long-term durability in Na metal-based energy storage, yet remains largely unexplored. Here, we report a previously unexplored design of spatially confined atomic Sn in hollow carbon spheres for homogeneous nucleation and dendrite-free growth. The designed architecture maximizes Sn utilization, prevents agglomeration, mitigates volume variation, and allows complete alloying-dealloying with high-affinity Sn as persistent nucleation sites, contrary to conventional spatially exposed large-size ones without dealloying. Thus, conformal deposition is achieved, rendering an exceptional capacity of 16 mAh cm−2 in half-cells and long cycling over 7000 hours in symmetric cells. Moreover, the well-known paradox is surmounted, delivering record-high Na utilization (e.g., 85%) and large capacity (e.g., 8 mAh cm−2) while maintaining extraordinary durability over 5000 hours, representing an important breakthrough for stabilizing Na anode

A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power. Tracking algorithms based on a kernel correlation filter are well-known and widely applied because of their high performance and speed, but when the target is in a complex background, it still can not adapt to the target scale change and occlusion, which will lead to template drift. In this paper, a fast multi-scale kernel correlation filter tracker based on adaptive template updating is proposed for common rigid targets. We introduce a simple scale pyramid on the basis of Kernel Correlation Filtering (KCF), which can adapt to the change in target size while ensuring the speed of operation. We propose an adaptive template updater based on the Mean of Cumulative Maximum Response Values (MCMRV) to alleviate the problem of template drift effectively when occlusion occurs. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods based on a kernel correlation filter

Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales

Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation

The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore multiscale feature fusion, and there is still much room for improvement. In this paper, we propose a novel multiresolution aggregation transformer UNet (MRA-TUNet) based on multiscale input and coordinate attention for medical image segmentation. It realizes multiresolution aggregation from the following two aspects: (1) On the input side, a multiresolution aggregation module is used to fuse the input image information of different resolutions, which enhances the input features of the network. (2) On the output side, an output feature selection module is used to fuse the output information of different scales to better extract coarse-grained information and fine-grained information. We try to introduce a coordinate attention structure for the first time to further improve the segmentation performance. We compare with state-of-the-art medical image segmentation methods on the automated cardiac diagnosis challenge and the 2018 atrial segmentation challenge. Our method achieved average dice score of 0.911 for right ventricle (RV), 0.890 for myocardium (Myo), 0.961 for left ventricle (LV), and 0.923 for left atrium (LA). The experimental results on two datasets show that our method outperforms eight state-of-the-art medical image segmentation methods in dice score, precision, and recall

Low-Light Image Enhancement Using Photometric Alignment with Hierarchy Pyramid Network

Low-light image enhancement can effectively assist high-level vision tasks that often fail in poor illumination conditions. Most previous data-driven methods, however, implemented enhancement directly from severely degraded low-light images that may provide undesirable enhancement results, including blurred detail, intensive noise, and distorted color. In this paper, inspired by a coarse-to-fine strategy, we propose an end-to-end image-level alignment with pixel-wise perceptual information enhancement pipeline for low-light image enhancement. A coarse adaptive global photometric alignment sub-network is constructed to reduce style differences, which facilitates improving illumination and revealing under-exposure area information. After the learned aligned image, a hierarchy pyramid enhancement sub-network is used to optimize image quality, which helps to remove amplified noise and enhance the local detail of low-light images. We also propose a multi-residual cascade attention block (MRCAB) that involves channel split and concatenation strategy, polarized self-attention mechanism, which leads to high-resolution reconstruction images in perceptual quality. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods in detail and color reproduction

Alteration of endocannabinoid system in human gliomas

Fujian Health-Education Research Grant, china [WKJ2008-2-45]; Xiamen Science and Technology Key Program Grant, China [3502Z20100006]Endocannabinoids are neuromodulatory lipids that mediate the central and peripheral neural functions. Endocannabinoids have demonstrated their anti-proliferative, anti-angiogenic and pro-apoptotic properties in a series of studies. In the present study, we investigated the levels of two major endocannabinoids, anandamide and 2-arachidonylglycerol (2-AG), and their receptors, CB1 and CB2, in human low grade glioma (WHO grade I-II) tissues, high grade glioma (WHO grade III-IV) tissues, and non-tumor brain tissue controls. We also measured the expressions and activities of the enzymes responsible for anandamide and 2-AG biosynthesis and degradation, that is, N-acylphosphatidylethanolamine-hydrolysing phospholipase D (NAPE-PLD), fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MGL), and diacylglycerol lipase-alpha (DGL), in the same samples. Liquid chromatographymass spectometry analysis showed that the levels of anandamide decreased, whereas the levels of 2-AG increased in glioma tissues, comparing to the non-tumor controls. The expression levels and activities of NAPE-PLD, FAAH and MGL also decreased in glioma tissues. Furthermore, quantitative-PCR analysis and western-blot analysis revealed that the expression levels of cananbinoid receptors, CB1 and CB2, were elevated in human glioma tissues. The changes of anandamide and 2-AG contents in different stages of gliomas may qualify them as the potential endogenous biomarkers for glial tumor malignancy