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In vivo assessment of dispersion affecting the propagation of visco-elastic waves in soft tissues is key to understand the rheology of human tissues. In this paper, the ability of the supersonic shear imaging (SSI) technique to generate planar shear waves propagating in tissues is fully exploited. First, by strongly limiting shear wave diffraction in the imaging plane, this imaging technique enables to discriminate between the usually concomitant influences of both medium rheological properties and diffraction affecting the shear wave dispersion. Second, transient propagation of these plane shear waves in soft tissues can be measured using echographic images acquired at very high frame. In vitro and in vivo experiments demonstrate that dispersion curves, which characterize the rheological behavior of tissues by measuring the frequency dependence of shear wave speed and attenuation, can be recovered in the 75-600 Hz frequency range. Based on a phase difference algorithm, the dispersion curves are computed in 1 cm 2 regions of interest from the acquired propagation movie. In vivo measurements in biceps brachii muscle and liver of three healthy volunteers show important differences in the rheological behavior of these different tissues. Liver tissue appears to be much more dispersive with a phase velocity ranging from ~ 1.5 m/s at 75 Hz to ~ 3 m/s at 500 Hz whereas muscle tissue shows an important anisotropy, shear waves propagating longitudinally to the muscular fibers are almost nondispersive while those propagating transversally are very dispersive with a shear wave speed ranging from 0.5 to 2 m/s between 75 and 500 Hz. The estimation of dispersion curves is local and can be performed separately in different regions of the organ. This signal processing approach based on the SSI modality introduces the new concept of In vivo shear wave spectroscopy (SWS) that could become an additional tool for tissue characterization. This paper demonstrates the in vivo ability of this SWS to quantify both local shear elasticity and dispersion in real time.

影响软组织中粘弹性波传播的色散现象的体内评估是理解人体组织流变学的关键。本文充分利用了超音速剪切成像（SSI）技术在组织中产生平面剪切波的能力。首先，通过强烈限制剪切波在成像平面内的衍射，该成像技术能够区分通常同时存在的介质流变特性和影响剪切波色散的衍射效应。其次，可以利用超高帧率获取的超声图像测量这些平面剪切波在软组织中的瞬态传播。体外和体内实验证明，通过测量剪切波速度和衰减的频率依赖性，可以在75-600 Hz频率范围内恢复表征组织流变行为的色散曲线。基于相位差算法，从获取的传播电影中在1 cm²的感兴趣区域计算色散曲线。在三名健康志愿者的肱二头肌和肝脏中的体内测量显示，这些不同组织的流变行为存在重要差异。肝脏组织表现出更强的色散性，相速度从75 Hz时的约1.5 m/s变化到500 Hz时的约3 m/s；而肌肉组织表现出显著的各向异性，沿肌纤维纵向传播的剪切波几乎无色散，而横向传播的剪切波色散很强，剪切波速度在75至500 Hz之间介于0.5至2 m/s。色散曲线的估计是局部的，可以在器官的不同区域分别进行。这种基于SSI模态的信号处理方法引入了体内剪切波光谱的新概念，可能成为组织表征的附加工具。本文展示了该SWS在体内实时量化局部剪切弹性和色散的能力。

This paper presents a quantitative comparison of three types of information available for 52 patients scheduled for carotid endarterectomy: subjective classification of the ultrasound images obtained during scanning before operation, first- and second-order statistical features extracted from regions of the plaque in still ultrasound images from three orthogonal scan planes and finally a histologi...

本文对52名计划进行颈动脉内膜切除术患者的三种可用信息进行了定量比较：术前扫描过程中获得的超声图像的主观分类、从三个正交扫描平面的静止超声图像中的斑块区域提取的一阶和二阶统计特征，最后是组织学检查。

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts ( i.e. , modalities). Fusing this multi-modal data has proven particularly effective for boosting model performance in many tasks. However, due to poor data quality and frequent patient dropout, collecting all modalities for every patient remains a challenge. Medical image synthesis has been proposed as an effective solution, where any missing modalities are synthesized from the existing ones. In this paper, we propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis, which learns a mapping from multi-modal source images ( i.e. , existing modalities) to target images ( i.e. , missing modalities). In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality, and a fusion network is employed to learn the common latent representation of multi-modal data. Then, a multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality, acting as a generator to synthesize the target images. Moreover, a layer-wise multi-modal fusion strategy effectively exploits the correlations among multiple modalities, where a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical image synthesis methods.

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A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

提出了一种解决多模态医学图像配准问题的新方法，该方法使用信息论中的基本概念——互信息（MI）或相对熵作为新的匹配准则。本文提出的方法应用互信息来测量两幅图像中对应体素图像强度之间的统计依赖性或信息冗余，并假设当图像几何对齐时互信息最大。互信息最大化是一个非常通用且强大的准则，因为未对该依赖关系的性质做任何假设，也未对所涉及模态的图像内容施加限制性约束。通过与立体定向配准解决方案的比较，验证了互信息准则在计算机断层扫描（CT）、磁共振（MR）和光子发射断层扫描（PET）图像刚体配准中的准确性，同时评估了其对于实现问题（如插值和优化）和图像内容（包括部分重叠和图像退化）的鲁棒性。结果表明，可以完全自动地实现相对于立体定向参考解的亚体素精度，且无需任何预先分割、特征提取或其他预处理步骤，这使得该方法非常适合临床应用。

Using statistical methods the reconstruction of positron emission tomography (PET) images can be improved by high-resolution anatomical information obtained from magnetic resonance (MR) images. The authors implemented two approaches that utilize MR data for PET reconstruction. The anatomical MR information is modeled as a priori distribution of the PET image and combined with the distribution of t...

利用统计方法，正电子发射断层成像（PET）图像的重建可以通过从磁共振（MR）图像中获得的高分辨率解剖信息得到改善。作者实现了两种利用MR数据进行PET重建的方法。解剖MR信息被建模为PET图像的先验分布，并与...分布相结合。

The accuracy of image-guided neurosurgery generally suffers from brain deformations due to intraoperative changes. These deformations cause significant changes of the anatomical geometry (organ shape and spatial interorgan relations), thus making intraoperative navigation based on preoperative images error prone. In order to improve the navigation accuracy, the authors developed a biomechanical mo...

图像引导神经外科的准确性通常受到术中变化导致的脑变形的影响。这些变形会引起解剖几何（器官形状和空间器官间关系）的显著变化，从而使基于术前图像的术中导航容易出错。为了提高导航精度，作者开发了一种生物力学模型...

Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

卷积神经网络（CNN）是深度学习网络架构，推动了计算机视觉应用中一系列最先进技术，并在医学图像分析中越来越受欢迎。然而，CNN的训练耗时且具有挑战性。在医学图像分析任务中，大多数训练样本易于分类，因此贡献很...

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

提出了一种用于视网膜二维彩色图像中血管自动分割的方法。该方法可用于视网膜图像的计算机分析，例如糖尿病视网膜病变的自动筛查。该系统基于提取图像脊线，这些脊线与血管中心线大致重合。脊线用于组成线元素形式的基元...

A method is described for the automated detection of microcalcifications in digitized mammograms. The method is based on the Laplacian scale-space representation of the mammogram only. First, possible locations of microcalcifications are identified as local maxima in the filtered image on a range of scales. For each finding, the size and local contrast is estimated, based on the Laplacian response...

描述了一种用于数字化乳腺X线摄影中微钙化自动检测的方法。该方法仅基于乳腺X线摄影的拉普拉斯尺度空间表示。首先，在一系列尺度上，将微钙化的可能位置识别为滤波后图像中的局部最大值。对于每个发现，根据拉普拉斯响应估计其大小和局部对比度...

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.

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The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained. >

作者定义了用于从投影数据恢复图像的标准算法（如期望最大化，EM）的有序子集处理。有序子集方法将投影数据分组为有序的子集序列（或块）。有序子集EM的一次迭代定义为一次通过所有子集，在每个子集中使用当前估计来初始化应用

The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.

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Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

从头训练深度卷积神经网络（CNN）是困难的，因为它需要大量标记的训练数据和丰富的专业知识来确保正确收敛。一个有前景的替代方案是微调一个已经使用大量标记自然图像预训练的CNN。然而，自然图像与医学图像之间的显著差异...

The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRIs) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout...

探索使用小波变换来检测在两种不同实验条件下获取的脑功能磁共振图像（fMRIs）之间的差异。该方法得益于这样一个事实：平滑且空间局部化的信号可以用少量局部小波系数表示，而白噪声的功率均匀分布在整个...

Guidance systems designed for neurosurgery, hip surgery, spine surgery and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization and is, thus, to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. For most registration tasks, the most important error measure is target registration error (TRE), which is the distance after registration between corresponding points not used in calculating the registration transform. Here, the authors derive an approximation to the distribution of TRE; this is an extension of previous work that gave the expected squared value of TRE. They show the distribution of the squared magnitude of TRE and that of the component of TRE in an arbitrary direction. Using numerical simulations, the authors show that their theoretical results are a close match to the simulated ones.

设计用于神经外科、髋关节手术、脊柱手术及其他相对刚性解剖结构入路的导航系统可利用刚体变换实现图像配准。这些系统通常依赖于基于点的配准来确定变换，且许多此类系统使用附着式基准标记来建立用于配准的精确基准点，这些点通过某些基准定位过程确定。准确性对这些系统至关重要，了解准确性的水平也同样重要。基于标记的系统（尤其是标记植入骨内的系统）的一个优点是，配准误差仅取决于基准定位，因此在很大程度上独立于所配准的具体对象。因此，基于标记系统的临床准确性应可通过使用体模或先前患者的实验测量来预测。对于大多数配准任务，最重要的误差指标是目标配准误差（TRE），即配准后未用于计算配准变换的对应点之间的距离。在此，作者推导了TRE分布的近似值；这是先前工作的扩展，先前工作给出了TRE的期望平方值。他们展示了TRE平方幅度以及TRE沿任意方向分量的分布。通过数值模拟，作者表明他们的理论结果与模拟结果紧密吻合。

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

我们提出了一种用于功能磁共振成像（fMRI）数据的概率独立成分分析（ICA）集成方法，该方法允许在高斯噪声存在下进行非方形混合。为了避免过拟合，我们通过对数据真实维度的贝叶斯分析（即激活和非高斯噪声源的数量）来客观估计高斯噪声的量。这使我们能够进行概率建模，并实现数据的渐近唯一分解。它减少了解释问题，因为每个最终的独立成分现在更可能仅由一个物理或生理过程引起。我们还描述了标准ICA的其他改进，例如时间预白化和时间序列的方差归一化，后者在存在弱激活时的降维背景下特别有用。我们讨论了关于源过程时空性质的先验信息的使用，以及使用高斯混合模型进行推理的备择假设检验方法。我们通过在真实和人工fMRI数据上展示和评估我们方法的性能，并与经典ICA和GLM分析得到的时空准确性进行比较。

Presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, the authors consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classe...

提出了一种全自动的三维脑组织分类方法用于磁共振（MR）图像。由于部分体积效应，MR图像体素可能由多种组织类型混合而成。因此，作者认为在脑数据集中不仅存在三种主要脑组织类型：灰质、白质和脑脊液，称为纯类...

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 $\times$ 3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

在脑肿瘤中，胶质瘤是最常见且最具侵袭性的，其最高级别会导致预期寿命极短。因此，治疗计划是改善肿瘤患者生活质量的关键阶段。磁共振成像（MRI）是一种广泛用于评估这些肿瘤的成像技术，但MRI产生的大量数据使得手动分割在合理时间内难以完成。

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step ...

中文摘要翻译待生成

The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used in combination, cannot distinguish accurate from inaccurate registrations. To this end, we introduce a “registration” algorithm that generates highly inaccurate image transformations, yet performs extremely well in terms of the surrogate measures. Of the tested criteria, only overlap scores of localized anatomical regions reliably distinguish reasonable from inaccurate registrations, whereas image similarity and tissue overlap do not. We conclude that tissue overlap and image similarity, whether used alone or together, do not provide valid evidence for accurate registrations and should thus not be reported or accepted as such.

非刚性图像配准的准确性通常使用替代指标来近似，例如组织标签重叠分数、图像相似性、图像差异或变换逆一致性误差。本文提供实验证据表明，这些指标即使组合使用，也无法区分准确配准和不准确配准。为此，我们引入了一种“配准”算法，该算法生成高度不准确的图像变换，但在替代指标方面表现极好。在测试的标准中，只有局部解剖区域的重叠分数能够可靠地区分合理配准与不准确配准，而图像相似性和组织重叠则不能。我们得出结论，组织重叠和图像相似性单独或一起使用都不能为准确配准提供有效证据，因此不应将其作为准确配准的报告或接受标准。

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using ${\cal L}^{2}$ Lebesgue measure of the $\gamma$ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., ${\cal H}^{1}$ Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.

血管结构的自动检测对于血管疾病的更好管理变得越来越重要。在本文中，我们提出了一种新的无限活动轮廓模型，该模型利用图像的混合区域信息来解决这个问题。具体来说，通过使用边界γ邻域的L^2 Lebesgue测度提供的无限周长正则化器，比基于特征边界长度（即H^1 Hausdorff测度）的传统模型能够更好地检测小的振荡（分支）结构。此外，为了获得更好的通用分割性能，所提出的模型利用了不同类型区域信息的优势，例如强度信息和基于局部相位的增强图的组合。基于局部相位的增强图因其在保持血管边缘方面的优越性而被使用，而给定的图像强度信息将保证特征的正确分割。我们通过将所提出的模型应用于三个公开的视网膜图像数据集（两个彩色眼底摄影数据集和一个荧光血管造影数据集）来评估其性能。与其他广泛使用的无监督和监督方法相比，所提出的模型优于其竞争对手。例如，在DRIVE数据集上实现的灵敏度（0.742）、特异性（0.982）和准确率（0.954）非常接近第二位观察者的标注结果。

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

本文对基于互信息的医学图像配准文献进行了概述。本综述旨在三个方面：为初入该领域者提供介绍，为领域内工作者提供概览，为寻找特定应用文献者提供参考。方法根据基于互信息配准的不同方面进行分类。主要分为方法论和应用两个方面。方法论部分描述了在图像预处理、灰度插值、优化、互信息测度的适应性调整以及不同类型的几何变换等方面的选择。应用部分提供了关于不同模态、患者间配准及不同解剖对象的文献参考。还包括了涉及互信息的比较研究。文章从熵和互信息的描述开始，以对过去成就和未来挑战的讨论结束。

In a previous study (J.B. West et al., J. Comput. Assist. Tomogr., vol. 21, p. 554-66, 1997) the authors demonstrated that automatic retrospective registration algorithms can frequently register magnetic resonance (MR) and computed tomography (CT) images of the brain with an accuracy of better than 2 mm, but in that same study the authors found that such algorithms sometimes fail, leading to error...

在之前的一项研究（J.B. West等人，《计算机辅助断层扫描杂志》，第21卷，第554-66页，1997年）中，作者证明了自动回顾性配准算法通常能够以优于2毫米的精度配准脑部磁共振（MR）和计算机断层扫描（CT）图像，但在同一研究中，作者发现此类算法有时会失败，导致误差...

Preliminary evaluation of recently developed large-area flat panel detectors (FPDs) indicates that FPDs have some potential advantages: compactness, absence of geometric distortion and veiling glare with the benefits of high resolution, high detective quantum efficiency (DQE), high frame rate and high dynamic range, small image lag (<1%), and excellent linearity (/spl sim/1%). The advantages of the new FPD make it a promising candidate for cone-beam volume computed tomography (CT) angiography (CBVCTA) imaging. The purpose of this study is to characterize a prototype FPD-based imaging system for CBVCTA applications. A prototype FPD-based CBVCTA imaging system has been designed and constructed around a modified GE 8800 CT scanner. This system is evaluated for a CBVCTA imaging task in the head and neck using four phantoms and a frozen rat. The system is first characterized in terms of linearity and dynamic range of the detector. Then, the optimal selection of kVps for CBVCTA is determined and the effect of image lag and scatter on the image quality of the CBVCTA system is evaluated. Next, low-contrast resolution and high-contrast spatial resolution are measured. Finally, the example reconstruction images of a frozen rat are presented. The results indicate that the FPD-based CBVCT can achieve 2.75-1p/mm spatial resolution at 0% modulation transfer function (MTF) and provide more than enough low-contrast resolution for intravenous CBVCTA imaging in the head and neck with clinically acceptable entrance exposure level. The results also suggest that to use an FPD for large cone-angle applications, such as body angiography, further investigations are required.

最近开发的大面积平板探测器（FPD）的初步评估表明，FPD具有一些潜在优势：紧凑性、无几何失真和光晕，同时具有高分辨率、高探测量子效率（DQE）、高帧率、高动态范围、小图像滞后（<1%）和优异的线性度（/spl sim/1%）。新型FPD的这些优势使其成为锥束容积计算机断层扫描（CT）血管成像（CBVCTA）的有前景候选。本研究旨在表征一种用于CBVCTA应用的原型FPD成像系统。围绕改进的GE 8800 CT扫描仪设计和构建了一种基于FPD的原型CBVCTA成像系统。使用四个体模和一只冷冻大鼠对该系统在头颈部CBVCTA成像任务中进行评估。首先根据探测器的线性度和动态范围进行表征。然后确定CBVCTA的最佳千伏峰值选择，并评估图像滞后和散射对CBVCTA系统图像质量的影响。接下来测量低对比度分辨率和高对比度空间分辨率。最后展示冷冻大鼠的重建图像示例。结果表明，基于FPD的CBVCT可以在0%调制传递函数（MTF）下达到2.75线对/毫米的空间分辨率，并在临床可接受的入射暴露水平下为头颈部静脉内CBVCTA成像提供足够高的低对比度分辨率。结果还提示，将FPD用于大锥角应用（如体部血管成像）需要进一步研究。

Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.

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