Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, the lateral strain, which is essential in several downstream tasks such as the inverse problem of elasticity imaging, remains a challenge. The lateral strain estimation is complicated since the motion and the sampling frequency in this direction are substantially lower than the axial one, and a lack of carrier signal in this direction. In computer vision applications, the axial and the lateral motions are independent. In contrast, the tissue motion pattern in USE is governed by laws of physics which link the axial and lateral displacements. In this paper, inspired by Hooke's law, we first propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose a constraint on the Effective Poisson's ratio (EPR) to improve the lateral strain estimation. In the next step, we propose self-supervised PICTURE (sPICTURE) to further enhance the strain image estimation. Extensive experiments on simulation, experimental phantom and in vivo data demonstrate that the proposed methods estimate accurate axial and lateral strain maps.

超声定位显微镜（ULM）是一种采用回声微泡（MB）定位的新兴技术，可对微循环进行精细样品和图像超声成像的衍射极限。常规的MB定位方法主要基于考虑MBS的特定点扩散函数（PSF），这导致由重叠MB，非平稳性PSF和谐波MB回波引起的信息丢失。因此，必须设计可以准确定位MB的方法，同时对MB非线性和扭曲MB PSF的MB浓度的变化有弹性。本文提出了一种基于变压器的MB本地化方法来解决此问题。我们采用了检测变压器（DETR）ARXIV：2005.12872，它是一种端到端对象识别方法，它使用基于集合的匈牙利损失和双方匹配来检测每个检测到的对象的唯一边界框。据作者所知，这是第一次将变形金刚用于MB本地化。为了评估拟议的策略，已经测试了使用转移学习原理检测MBS的预先培训的DETR网络的性能。我们已经在IEEE IUS Ultra-SR挑战组织者提供的随机选择的数据集的随机帧子集上进行了微调，然后使用交叉验证对其余进行测试。对于仿真数据集，本文支持基于变压器的解决方案以高精度为基础的MB本地化。

定量超声（QUS）提供了有关组织特性的重要信息。可以通过将包络数据分为小重叠贴片并计算不同的斑点统计信息，例如中Nakagami的参数和knody k-Distribution（HK-Distribution）来形成QUS参数图像。计算出的QUS参数图像可能是错误的，因为补丁中只有几个独立的样本可用。另一个挑战是，假定斑块内的包膜样品来自相同的分布，这一假设通常会违反，因为该组织通常不是同质的。在本文中，我们提出了一种基于卷积神经网络（CNN）的方法，以估算QUS参数图像而无需修补。我们构建一个从HK分布中采样的大数据集，具有随机形状和QUS参数值的区域。然后，我们使用众所周知的网络以多任务学习方式估算QUS参数。我们的结果证实，所提出的方法能够减少错误并改善QUS参数图像中的边界定义。

位移估计是几乎所有超声弹性图（使用）技术的关键步骤。与一般的光流问题相比，这两个主要功能使这项任务与众不同：超声射频（RF）数据的高频性质和位移字段上物理的管理定律。最近，已经对光流网络的体系结构进行了修改，以便能够使用RF数据。同样，通过考虑以第一和第二个衍生式正规化器的形式考虑位移连续性的先验知识，已采用半监督和无监督的技术来使用。尽管尝试了这些尝试，但尚未考虑组织压缩模式，并且假定轴向和横向方向的位移是独立的。然而，组织运动模式受使用的物理定律的控制，使轴向和横向位移高度相关。在本文中，我们提出了对无监督的正则弹性图（图）的身体启发的约束，在此我们对泊松比的约束以改善侧向位移估计值。有关幻影和体内数据的实验表明，图片大大提高了横向位移估计的质量。

尽管自动图像分析的重要性不断增加，但最近的元研究揭示了有关算法验证的主要缺陷。性能指标对于使用的自动算法的有意义，客观和透明的性能评估和验证尤其是关键，但是在使用特定的指标进行给定的图像分析任务时，对实际陷阱的关注相对较少。这些通常与（1）无视固有的度量属性，例如在存在类不平衡或小目标结构的情况下的行为，（2）无视固有的数据集属性，例如测试的非独立性案例和（3）无视指标应反映的实际生物医学领域的兴趣。该动态文档的目的是说明图像分析领域通常应用的性能指标的重要局限性。在这种情况下，它重点介绍了可以用作图像级分类，语义分割，实例分割或对象检测任务的生物医学图像分析问题。当前版本是基于由全球60多家机构的国际图像分析专家进行的关于指标的Delphi流程。

Bipedal robots have received much attention because of the variety of motion maneuvers that they can produce, and the many applications they have in various areas including rehabilitation. One of these motion maneuvers is walking. In this study, we presented a framework for the trajectory optimization of a 5-link (planar) Biped Robot using hybrid optimization. The walking is modeled with two phases of single-stance (support) phase and the collision phase. The dynamic equations of the robot in each phase are extracted by the Lagrange method. It is assumed that the robot heel strike to the ground is full plastic. The gait is optimized with a method called hybrid optimization. The objective function of this problem is considered to be the integral of torque-squared along the trajectory, and also various constraints such as zero dynamics are satisfied without any approximation. Furthermore, in a new framework, there is presented a constraint called impact invariance, which ensures the periodicity of the time-varying trajectories. On the other hand, other constraints provide better and more human-like movement.

The importance of humanoid robots in today's world is undeniable, one of the most important features of humanoid robots is the ability to maneuver in environments such as stairs that other robots can not easily cross. A suitable algorithm to generate the path for the bipedal robot to climb is very important. In this paper, an optimization-based method to generate an optimal stairway for under-actuated bipedal robots without an ankle actuator is presented. The generated paths are based on zero and non-zero dynamics of the problem, and according to the satisfaction of the zero dynamics constraint in the problem, tracking the path is possible, in other words, the problem can be dynamically feasible. The optimization method used in the problem is a gradient-based method that has a suitable number of function evaluations for computational processing. This method can also be utilized to go down the stairs.

Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environments can be harmful. As a result, there is a need to design a program that locates these differences using machine vision. The most important challenge of this problem is the change in lighting conditions and the presence of shadows in the scene. Therefore, the proposed methods must be resistant to these challenges. In this article, a method based on deep convolutional neural networks using transfer learning is introduced, which is trained with an intelligent data synthesis process. The results of this method are tested and presented on the dataset provided for this purpose. It is shown that the presented method is more efficient than other methods and can be used in a variety of real industrial environments.

This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous division and was further improved for the 2022 event, both of which resulting in first place finishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-defined track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.