心肌的准确分割和运动估计在临床领域一直很重要，这基本上有助于下游诊断。但是，现有方法不能始终保证心肌分割的形状完整性。此外，运动估计需要在不同帧上对心肌区域的点对应关系。在本文中，我们提出了一种新型的端到端深度统计形状模型，以关注具有形状完整性和边界对应关系的心肌分割。具体而言，心肌形状由固定数量的点表示，其变化是通过主成分分析（PCA）提取的。深神经网络用于预测转换参数（仿射和变形），然后将其用于将平均点云转转到图像域。此外，引入了一个可区分的渲染层，以将掩码的监督纳入框架中，以了解更准确的点云。通过这种方式，所提出的方法能够在不进行后处理的情况下始终如一地产生解剖上合理的分割掩码。此外，预测的点云还保证了顺序图像的边界对应关系，这有助于下游任务，例如心肌的运动估计。我们进行了几项实验，以证明在几个基准数据集上提出的方法的有效性。

深度学习方法为多级医学图像细分实现了令人印象深刻的表现。但是，它们的编码不同类别（例如遏制和排除）之间拓扑相互作用的能力受到限制。这些约束自然出现在生物医学图像中，对于提高分割质量至关重要。在本文中，我们介绍了一个新型的拓扑交互模块，将拓扑相互作用编码为深神经网络。该实施完全基于卷积，因此非常有效。这使我们有能力将约束结合到端到端培训中，并丰富神经网络的功能表示。该方法的功效在不同类型的相互作用上得到了验证。我们还证明了该方法在2D和3D设置以及跨越CT和超声之类的不同模式中的专有和公共挑战数据集上的普遍性。代码可在以下网址找到：https：//github.com/topoxlab/topointeraction

眼底摄影是诊断和监测眼部疾病的诊所的常规检查。但是，对于白内障患者，底眼图像始终会遭受由云晶状体引起的质量降解。降解阻止了眼科医生或计算机辅助系统可靠的诊断。为了提高临床诊断的确定性，已经提出了恢复算法来提高眼底图像的质量。不幸的是，这些算法的部署仍然存在挑战，例如收集足够的培训数据和保存视网膜结构。在本文中，为了规避严格的部署要求，从共享相同结构的合成数据中开发出了针对白内障底底图像的结构一致的恢复网络（SCR-NET）。白内障仿真模型首先是设计用于收集由白内障底面图像共享相同结构的合成性白内障集（SC）的。然后从SCS中提取高频组件（HFC）以约束结构一致性，从而强制执行SCR-NET中的结构保留。该实验证明了SCR-NET与最新方法和后续临床应用的比较中的有效性。该代码可从https://github.com/liamheng/arcnet-medical-image-enhancement获得。

除了每个像素精度外，拓扑正确性也对具有微尺度结构的图像的分割也至关重要，例如卫星图像和生物医学图像。在本文中，通过利用数字拓扑理论，我们识别对拓扑至关重要的图像中的位置。通过专注于这些关键的位置，我们提出了一种新的同谐扭曲损失来培训深度图像分割网络以获得更好的拓扑精度。为了有效地识别这些拓扑关键的位置，我们提出了一种利用距离变换的新算法。所提出的算法以及损耗函数，自然地推广到2D和3D设置中的不同拓扑结构。拟议的损失函数有助于深度网络在拓扑知识的指标方面实现更好的性能，优于最先进的拓扑保存分段方法。

Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation. Considering that internal dimensions in score-based generative models have a critical impact on estimating the gradient of the data distribution, we present a new idea, low-rank tensor assisted k-space generative model (LR-KGM), for parallel imaging reconstruction. This means that we transform original prior information into high-dimensional prior information for learning. More specifically, the multi-channel data is constructed into a large Hankel matrix and the matrix is subsequently folded into tensor for prior learning. In the testing phase, the low-rank rotation strategy is utilized to impose low-rank constraints on tensor output of the generative network. Furthermore, we alternately use traditional generative iterations and low-rank high-dimensional tensor iterations for reconstruction. Experimental comparisons with the state-of-the-arts demonstrated that the proposed LR-KGM method achieved better performance.

Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multifeature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.

Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation.

This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval.

随着卷积神经网络（CNN）的蓬勃发展，诸如VGG-16和Resnet-50之类的CNN广泛用作SAR船检测中的骨架。但是，基于CNN的骨干很难对远程依赖性进行建模，并且导致缺乏浅层特征图中缺乏足够的高质量语义信息，从而导致在复杂的背景和小型船只中的检测性能不佳。为了解决这些问题，我们提出了一种基于SWIN Transformer的SAR船检测方法，并提出了功能增强功能功能金字塔网络（FEFPN）。SWIN Transformer用作建模远程依赖性并生成层次特征图的骨架。提出了FEFPN，以进一步提高特征地图的质量，通过逐渐增强各级特征地图的语义信息，尤其是浅层中的特征地图。在SAR船检测数据集（SSDD）上进行的实验揭示了我们提出的方法的优势。

如何充分利用极化来增强合成孔径雷达（SAR）船舶分类仍然是一个尚未解决的问题。因此，我们提出了一个双极化信息引导网络（DPIG-NET）来解决它。