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We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.

我们提出了一种基于模型的图像重建框架，采用卷积神经网络（CNN）正则化先验。该公式提供了一种系统的方法，用于推导任意结构逆问题的深度学习架构。由于正向模型被明确考虑，与直接反演方法相比，一个参数更少的小型网络就足以捕获图像信息，从而减少对训练数据和训练时间的需求。由于我们依赖跨迭代共享权重的端到端训练，CNN权重是根据正向模型定制的，因此相比依赖预训练去噪器的方法提供了更好的性能。我们的实验表明，这种方法将迭代次数与网络复杂性分离，带来了好处，包括降低训练数据需求、减少过拟合风险以及显著减少内存占用的实现。我们建议通过在网络内使用数值优化块（例如共轭梯度算法）来强制执行数据一致性。与依赖近端梯度步骤来强制执行数据一致性的方法相比，这种方法每次迭代收敛更快。我们的实验表明，更快的收敛转化为更好的性能，尤其是在可用GPU内存限制迭代次数时。

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

显微细胞图像分析是生物学研究的基本工具。特别是，多变量荧光显微镜用于观察培养细胞的不同方面。目前，通过视觉检查单个细胞来执行分析任务仍然是常见做法，这既耗时又容易产生主观偏差。这使得自动细胞图像分析对于大规模、客观的细胞培养研究至关重要。传统上，自动细胞分析的任务通过使用图像分割方法提取细胞的位置和形状来实现。图像分割虽然是基础，但在计算机视觉中并非易事，也不鲁棒于图像质量变化。这使得用于细胞检测的图像分割半自动化，需要频繁调整参数。本文提出了一种基于滑动带滤波器（SBF）的多变量图像中细胞检测和形状估计的新方法。该滤波器的设计使其足以检测整体凸形状，因此在细胞检测中表现良好。此外，涉及的参数直观，因为它们直接与预期的细胞大小相关。使用SBF滤波器，我们检测细胞核和细胞质的位置和形状。基于每个细胞在细胞核和细胞质荧光通道中具有近似相同形状中心的假设，我们通过核检测指导细胞质形状估计，提高性能并减少误差。然后通过从核和细胞质通道收集证据来验证细胞检测。此外，我们还包括重叠校正和形状正则化步骤，进一步改进估计的细胞形状。该方法使用两个不同类型的数据集进行评估：一个包含1000个模拟细胞的20张模拟细胞培养图像基准集；一个包含1255个细胞、标记DNA和肌动蛋白的16张黑腹果蝇Kc167数据集。由于细胞形状的高度变异和细胞之间频繁的簇重叠，这两个图像数据集都提出了难题。在果蝇数据集上，我们的方法在核检测和细胞质检测上分别达到了95%/69%和82%/90%的精确率/召回率，总体准确率为76%。

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of $5.69\pm 2.41\ \mu{\rm m}$ was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of $5.71\pm 1.98\ \mu{\rm m}$ .

随着谱域光学相干断层扫描（OCT）的引入，与上一代时域OCT相比，常规采集的图像数据集要大得多。因此，对用于处理此类数据的三维分割方法的需求变得越来越重要。我们报告了一种图论分割方法，用于同时分割多个三维表面，该方法在成本函数方面保证最优，并且直接适用于三维谱域OCT图像数据的分割。我们提出了对一般分层图分割方法的两个扩展：能够结合变化的可行性约束和能够结合真实的区域信息。从13名受试者的13幅谱域OCT图像的训练集中学习了适当的可行性约束和成本函数。训练后，我们的方法在来自14名受试者的28幅图像的测试集上进行了测试。在分割七个表面（六层）时，以两位眼科医生手动描记的平均值作为参考标准，实现了$5.69±2.41\ \mu{\rm m}$的总体平均无符号边界定位误差。这一结果与测得的$5.71±1.98\ \mu{\rm m}$观察者间变异性非常接近。

Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, the authors first discuss the widely used methods based on...

功能磁共振成像（fMRI）提供高分辨率数据集，使研究人员能够获得认知过程中激活区域的准确描绘和敏感检测。为了保持这种无创技术的分辨率，在数据分析中需要精细的方法。本文首先讨论了基于...的广泛使用的方法。

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.

图像恢复中的一个关键问题是在保持相关图像信息完整性的同时去除噪声。去噪是提高图像质量以及改善定量成像分析所需所有任务性能的关键步骤。本文提出的方法基于非局部均值（NL-means）滤波器（Buades等人，2005）的三维优化分块版本。NL-means滤波器利用研究图像中信息的冗余性来去除噪声。NL-means滤波器在二维图像上的性能已经得到验证，但减少计算负担是将该方法扩展到三维图像的关键问题。为了解决这一问题，我们提出了降低计算复杂度的改进措施。这些不同的改进措施能够在保持NL-means滤波器性能的同时大幅缩短计算时间。然后，我们提出了一个全自动且优化的NL-means滤波器版本。我们对NL-means滤波器的贡献包括：1）自动调整平滑参数；2）选择最相关的体素；3）分块实现；4）并行计算。定量验证在BrainWeb（Collins等人，1998）生成的合成数据集上进行。结果表明，与经典NL-means滤波器实现以及其他两种经典去噪方法（各向异性扩散（Perona和Malik，1990）和全变分最小化过程（Rudin等人，1992））相比，我们的优化NL-means滤波器在精度（以峰值信噪比衡量）方面表现更优，且计算时间更短。最后，给出了真实数据的定性结果。

From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semiland...

作者利用一组同一解剖结构的纵向三维扫描，通过线性形状模型精确模拟了时间上的形状和大小变化。基于六名患者的31次下颌骨CT扫描，使用形状特征和一种名为几何约束扩散的新算法自动找到了14851个半地标。半地标...

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN)...

使用标准苏木精和伊红染色的癌组织病理学图像中细胞核的检测和分类由于细胞异质性而具有挑战性。深度学习已在多项研究中显示出对组织病理学图像产生令人鼓舞的结果。在本文中，我们提出了一种空间约束卷积神经网络（SC-CNN）...

Presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.

提出了一种新的图像配准方法，该方法基于联合估计两幅图像之间的正向和反向变换，同时限制这些变换互为逆变换。这种方法产生了一致性的变换集，其配准误差小于传统方法独立估计正向和反向变换的误差，即具有更好的对应性。变换通过迭代估计，并通过约束其遵循连续介质力学定律来限制拓扑保持。变换通过傅里叶级数参数化，以对角化连续介质力学约束施加的协方差结构，并提供计算高效的数值实现。使用线性弹性材料约束的结果在磁共振和X射线计算机断层扫描图像数据上展示。结果表明，在线性弹性约束下联合估计一致性的正向和反向变换，比单独使用任一约束或完全不使用约束给出更好的配准结果。

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D p...

我们提出了一种新颖的计算机辅助检测（CAD）系统，用于肺结节的检测，该系统使用多视角卷积网络（ConvNets），自动从训练数据中学习判别特征。网络输入是通过结合三个专门为实性、亚实性和大结节设计的候选检测器获得的结节候选。对于每个候选，一组二维...

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.

青光眼是一种导致视力丧失的慢性眼病。由于无法治愈，及时检测该疾病至关重要。目前使用眼压（IOP）的测试对于基于人群的青光眼筛查不够敏感。视网膜眼底图像中的视神经头评估更有前景且更优越。本文提出使用超像素分类进行视盘和视杯分割，用于青光眼筛查。在视盘分割中，使用直方图和中心环绕统计将每个超像素分类为盘或非盘。计算自我评估可靠性分数以评估自动视盘分割的质量。对于视杯分割，除了直方图和中心环绕统计外，还将位置信息纳入特征空间以提高性能。所提出的分割方法在650张图像的数据集上进行了评估，这些图像的视盘和视杯边界由训练有素的专业人员手动标记。实验结果显示，视盘和视杯分割的平均重叠误差分别为9.5%和24.1%。结果还显示，随着可靠性分数的降低，重叠误差增加，这证明了自我评估的有效性。分割后的视盘和视杯随后用于计算杯盘比，以进行青光眼筛查。我们提出的方法在两个数据集中实现了0.800和0.822的曲线下面积，高于其他方法。这些方法可用于分割和青光眼筛查。自我评估将作为大误差病例的指标，并增强自动分割和筛查的临床部署。

This paper presents microwave tomographic reconstructions of the complex permittivity of lossy dielectric objects immersed in water from experimental multiview near-field data obtained with a 2.35-GHz planar active microwave camera. An iterative reconstruction algorithm based on the Levenberg-Marquardt method was used to solve the nonlinear matrix equation which results when applying a moment meth...

本文介绍了利用2.35 GHz平面有源微波相机获得的实验多视近场数据，对浸没在水中的有耗介电物体的复介电常数进行微波层析重建。采用基于Levenberg-Marquardt方法的迭代重建算法来求解应用矩量法时产生的非线性矩阵方程...

Presents an image reconstruction method for positron-emission tomography (PET) based on a penalized, weighted least-squares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, the author argues statistically that a least-squares objective function is as appropriate, if not more so, than the popular Poisson likelihood objective. The author proposes a simple dat...

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By exploiting the correlation of ultrasound speckle patterns that result from scattering by underlying tissue elements, two-dimensional tissue motion theoretically can be recovered by tracking the apparent movement of the associated speckle patterns. Speckle tracking, however, is an ill-posed inverse problem because of temporal decorrelation of the speckle patterns and the inherent low signal-to-n...

通过利用由底层组织元素散射产生的超声斑点模式的相关性，理论上可以通过跟踪相关斑点模式的表观运动来恢复二维组织运动。然而，由于斑点模式的时间去相关性和固有的低信噪比，斑点跟踪是一个不适定的逆问题。

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions. The ...

中文摘要翻译待生成

This paper describes research work motivated by an innovative medical application: computer-assisted transbronchial biopsy. This project involves the registration, with no external localization device, of a preoperative three-dimensional (3-D) computed tomography (CT) scan of the thoracic cavity (showing a tumor that requires a needle biopsy), and an intraoperative endoscopic two-dimensional (2-D)...

本文描述了由一项创新医学应用（计算机辅助经支气管活检）所驱动的研究工作。该项目涉及在无外部定位设备的情况下，将术前三维胸部CT扫描（显示需要针吸活检的肿瘤）与术中二维内镜图像进行配准...

Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.

中文摘要翻译待生成

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net ) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net , a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

中文摘要翻译待生成

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet , a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction , which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available.

最近，密集连接在计算机视觉中引起了广泛关注，因为它们促进了训练期间的梯度流动和隐式深度监督。特别是，DenseNet以前馈方式将每一层连接到其他所有层，并在自然图像分类任务中表现出令人印象深刻的性能。我们提出了HyperDenseNet，一种3D全卷积神经网络，将密集连接的定义扩展到多模态分割问题。每个成像模态都有一个路径，密集连接不仅发生在同一路径内的层对之间，还发生在不同路径的层对之间。这与现有的多模态CNN方法形成对比，这些方法完全依赖单个联合层（或抽象级别）来融合多个模态，通常是在网络的输入或输出处。因此，提出的网络有完全的自由度来学习模态之间更复杂的组合，在所有抽象级别内部和之间，这显著增加了学习表示。我们在两个不同且高度竞争的多模态脑组织分割挑战中报告了广泛的评估，即iSEG 2017和MRBrainS 2013，前者关注六个月婴儿数据，后者关注成人图像。HyperDenseNet在许多最先进的分割网络上取得了显著改进，在两个基准测试中均排名第一。我们还提供了特征重用的全面实验分析，证实了超密集连接在多模态表示学习中的重要性。我们的代码已公开可用。

A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to reconstruct artifact-free images. The image reconstructed from estimates using this tec...

中文摘要翻译待生成

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

数字显微组织图像中的细胞核分割能够提取高质量的特征，用于核形态学测量和计算病理学中的其他分析。传统的图像处理技术，如Otsu阈值分割和分水岭分割，在染色质稀疏和密集细胞核等具有挑战性的情况下效果不佳。相比之下，基于机器学习的分割可以泛化到各种细胞核外观。然而，训练机器学习算法需要大量已标注细胞核的图像数据集。公开可访问的标注数据集，以及广泛认可的用于比较技术的指标，已经极大地推动了其他图像分类问题（尤其是对象识别）的创新和进展。受其成功启发，我们引入了一个大型公开可访问的苏木精和伊红（H&E）染色组织图像数据集，其中包含超过21000个精心标注的细胞核边界，其质量由医学医生验证。由于我们的数据集来自多家医院，并包含来自多个患者、疾病状态和器官的多种细胞核外观，基于该数据集训练的技术很可能具有良好的泛化能力，并可直接应用于其他H&E染色图像。我们还提出了一种新的评估细胞核分割结果的指标，该指标统一惩罚对象级和像素级错误，而以往的指标只惩罚一种错误。我们还提出了一种基于深度学习的分割技术，特别强调识别细胞核边界，包括接触或重叠的细胞核之间的边界，并且在各种测试图像上表现良好。

The distorted Born iterative method (DBIM) is used to solve two-dimensional inverse scattering problems, thereby providing another general method to solve the two-dimensional imaging problem when the Born and the Rytov approximations break down. Numerical simulations are performed using the DBIM and the method proposed previously by the authors (Int. J. Imaging Syst. Technol., vol.1, no.1, p.100-8...

中文摘要翻译待生成

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the...

本文提出了一种新的用于视网膜图像血管分割的监督方法。该方法将分割任务重新塑造为从视网膜图像到血管图的跨模态数据转换问题。提出了一种具有强归纳能力的宽深度神经网络来建模这种转换，并提出了一种高效的训练策略。

In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their ...

本文采用多分辨率分析，特别是离散小波变换模极大值（mod-max）方法，用于提取乳腺肿块形状特征。这些形状特征用于分类系统，将肿块分为圆形、结节状或星状。将多分辨率形状特征与传统的单分辨率形状特征进行比较...

Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q -sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q -ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.

基于扩散磁共振（MR）信号与潜在扩散位移之间的傅里叶变换关系，推导出一个新的关系式，可直接从扩散MR信号估计自旋分布函数（SDF）。这一关系引出了一项称为广义q-采样成像（GQI）的成像方法，该方法可以从q-球成像（QBI）中使用的壳采样方案或扩散频谱成像（DSI）中使用的网格采样方案获得SDF。通过仿真研究和与QBI及DSI的体内实验比较，评估了GQI的准确性。仿真结果表明，GQI的准确性与QBI和DSI相当。GQI的仿真研究还显示，一个名为定量各向异性的各向异性指数与所解析纤维成分的体积分数相关。GQI的体内图像显示，SDF模式与QBI或DSI重建的ODF相似。从GQI生成的纤维追踪也与从QBI和DSI生成的相似。总之，所提出的GQI方法可应用于网格或壳采样方案，并能提供关于交叉纤维的方向和定量信息。

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adapti...

在广泛的语义分割任务中，全卷积神经网络已成功用于实现最先进的性能。全卷积网络的架构创新主要集中在改进空间编码或网络连接以辅助梯度流。在本文中，我们旨在向另一个方向，即自适应地重新校准学习到的特征图...