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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

有限混合(FM)模型是最常用的脑部磁共振(MR)图像统计分割模型，因为其数学形式简单且理想脑MR图像具有分段常数特性。然而，作为基于直方图的模型，FM有一个固有的局限性——未考虑空间信息。这导致FM模型仅适用于噪声水平低的明确定义图像；不幸的是，由于部分容积效应和偏置场失真等伪影，情况往往并非如此。在这些条件下，基于FM模型的方法会产生不可靠的结果。本文作者提出了一种新颖的隐马尔可夫随机场(HMRF)模型，这是一种由MRF生成的随机过程，其状态序列无法直接观测，但可以通过观测间接估计。从数学上可以证明，FM模型是HMRF模型的退化形式。HMRF模型的优势在于通过相邻位置的相互影响来编码空间信息的方式。尽管其他研究者已将MRF建模应用于MR图像分割，但大多数报道的方法仅限于在基于FM模型的方法中将MRF作为通用先验。为了拟合HMRF模型，使用了EM算法。作者表明，通过将HMRF模型和EM算法结合到HMRF-EM框架中，可以实现准确且鲁棒的分割。更重要的是，HMRF-EM框架可以轻松与其他技术结合。例如，作者展示了如何将Guillemaud和Brady(1997)的偏置场校正算法纳入该框架，从而实现脑MR图像分割的三维全自动方法。

Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented.

描述了一种用于脑部磁共振（MR）图像的全自动三维（3-D）分割技术。通过马尔可夫随机场（MRF），该分割算法捕捉了MR图像中特别重要的三个特征，即组织强度的非参数分布、邻域相关性和信号不均匀性。详细的模拟和真实MR图像展示了分割算法的性能。特别地，定量分析了噪声、不均匀性、平滑和结构厚度的影响。即使是单回波MR图像也能很好地分类为灰质、白质、脑脊液、头皮骨骼和背景。文中介绍了模拟退火和迭代条件模式两种实现方法。

Coherent X-ray scattering is related to the electron density distribution by a Fourier transform, and therefore a window into the microscopic structures of biological samples. Current techniques of scattering rely on small-angle measurements from highly collimated X-ray beams produced from synchrotron light sources. Imaging of the distribution of scattering provides a new contrast mechanism which is different from absorption radiography, but is a lengthy process of raster or line scans of the beam over the object. Here, we describe an imaging technique in the spatial frequency domain capable of acquiring both the scattering and absorption distributions in a single exposure. We present first results obtained with conventional X-ray equipment. This method interposes a grid between the X-ray source and the imaged object, so that the grid-modulated image contains a primary image and a grid harmonic image. The ratio between the harmonic and primary images is shown to be a pure scattering image. It is the auto-correlation of the electron density distribution at a specific distance. We tested a number of samples at 60–200 nm autocorrelation distance, and found the scattering images to be distinct from the absorption images and reveal new features. This technique is simple to implement, and should help broaden the imaging applications of X-ray scattering.

相干X射线散射通过傅里叶变换与电子密度分布相关，因此是生物样品微观结构的一个窗口。当前的散射技术依赖于同步辐射光源产生的高度准直X射线束的小角测量。散射分布的成像提供了一种新的对比机制……

Although current edge-following schemes can be very efficient in determining coronary boundaries, they may fail when the feature to be followed is disconnected (and the scheme is unable to bridge the discontinuity) or branch points exist where the best path to follow is indeterminate. Here, the authors present new deformable spline algorithms for determining vessel boundaries, and enhancing their centerline features. A bank of even and odd S-Gabor filter pairs of different orientations are convolved with vascular images in order to create an external snake energy field. Each fitter pair will give maximum response to the segment of vessel having the same orientation as the filters. The resulting responses across filters of different orientations are combined to create an external energy field for snake optimization. Vessels are represented by B-Spline snakes, and are optimized on filter outputs with dynamic programming. The points of minimal constriction and the percent-diameter stenosis are determined from a computed vessel centerline. The system has been statistically validated using fixed stenosis and flexible-tube phantoms. It has also been validated on 20 coronary lesions with two independent operators, and has been tested for interoperator and intraoperator variability and reproducibility. The system has been found to be specially robust in complex images involving vessel branchings and incomplete contrast filling.

尽管当前的边缘跟踪算法在确定冠状动脉边界时可以非常高效，但当要跟踪的特征不连续（且算法无法桥接不连续性）或存在分支点（最佳跟踪路径不确定）时，它们可能会失败。本文作者提出了新的可变形样条算法，用于确定血管边界并增强其...

A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B -spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as “N4ITK,” available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized $^{3}{\rm He}$ lung image data, and 9.4T postmortem hippocampus data.

提出了一种流行的非参数非均匀强度归一化（N3）算法的变体，用于偏置场校正。鉴于N3的出色性能及其公开可用性，它已成为多项评估研究的主题。这些研究表明，与B样条最小二乘拟合相关的某些参数的重要性。我们建议用...

The Fourier inversion method for reconstruction of images in computerized tomography has not been widely used owing to the perceived difficulty of interpolating from polar or other measurement grids to the Cartesian grid required for fast numerical Fourier inversion. Although the Fourier inversion method is recognized as being computationally faster than the back-projection method for parallel ray...

中文摘要翻译待生成

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

本文报告了与MICCAI 2012和2013会议联合组织的多模态脑肿瘤图像分割基准（BRATS）的设置和结果。我们将20种最先进的肿瘤分割算法应用于一组65个低级别和高级别胶质瘤患者的多对比度MR扫描（由最多四位评分者手动标注）以及65个可比较的生成扫描...

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms.

青光眼是一种导致不可逆视力丧失的慢性眼病。现有的自动筛查方法大多先分割主要结构，然后计算临床指标以检测和筛查青光眼。然而，这些基于测量的方法严重依赖于分割精度，并忽略了各种视觉特征。在本文中，我们介绍...

A new model-based vision (MBV) algorithm is developed to find regions of interest (ROI's) corresponding to masses in digitized mammograms and to classify the masses as malignant/benign. The MBV algorithm is comprised of 5 modules to structurally identify suspicious ROI's, eliminate false positives, and classify the remaining as malignant or benign. The focus of attention module uses a difference of Gaussians (DoG) filter to highlight suspicious regions in the mammogram. The index module uses tests to reduce the number of nonmalignant regions from 8.39 to 2.36 per full breast image. Size, shape, contrast, and Laws texture features are used to develop the prediction module's mass models. Derivative-based feature saliency techniques are used to determine the best features for classification. Nine features are chosen to define the malignant/benign models. The feature extraction module obtains these features from all suspicious ROI's. The matching module classifies the regions using a multilayer perceptron neural network architecture to obtain an overall classification accuracy of 100% for the segmented malignant masses with a false-positive rate of 1.8 per full breast image. This system has a sensitivity of 92% for locating malignant ROI's. The database contains 272 images (12 b, 100 /spl mu/m) with 36 malignant and 53 benign mass images. The results demonstrate that the MBV approach provides a structured order of integrating complex stages into a system for radiologists.

提出了一种新的基于模型的视觉（MBV）算法，用于在数字化乳腺X光片中识别与肿块对应的感兴趣区域（ROI），并将肿块分类为恶性/良性。该MBV算法由5个模块组成，用于结构性地识别可疑ROI、消除假阳性，并将剩余的分类为恶性或良性。注意力聚焦模块使用高斯差...

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website ( http://empire10.isi.uu.nl ). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

EMPIRE10（2010年肺图像配准方法评估）是一个公开平台，旨在公平且有意义地比较应用于患者内胸部CT图像对数据库的配准算法。评估非刚性配准技术是一项重要任务。然而，研究人员通常仅在自己的数据上进行测试，而这些数据差异很大，因此过去几乎无法对不同配准算法进行可靠的评估和比较。本文介绍了EMPIRE10启动阶段的结果，该阶段对来自领先学术和工业研究小组的20种独立算法进行了全面评估和比较。所有算法均应用于相同的30对胸部CT图像。算法设置和参数由精通各自方法配置的研究人员选择，评估独立进行，对所有参与者采用相同标准。所有结果均发布在EMPIRE10网站（http://empire10.isi.uu.nl）上。该挑战仍在进行中，并对新参与者开放。截至撰写本文时，已发布24种算法的完整结果。本文详细介绍了挑战的组织、数据和评估方法，以及初始启动阶段20种算法的结果，并讨论了知识收获和未来工作。

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

图像识别取得了显著进展，这主要得益于大规模标注数据集和深度卷积神经网络（CNN）的可用性。CNN能够从足够的训练数据中学习数据驱动的、高度代表性的层次化图像特征。然而，在医学成像领域获得像ImageNet那样全面标注的数据集仍然是一个挑战...

Recently, imaging techniques have become available which permit nondestructive analysis of the three-dimensional (3-D) architecture of trabecular bone (TB), which forms a network of interconnected plates and rods. Most osteoporotic fractures occur at locations rich in TB, which has spurred the search for architectural parameters as determinants of bone strength. Here, the authors present a new approach to quantitative characterization of the 3-D microarchitecture of TB, based on digital topology. The method classifies each voxel of the 3-D structure based on the connectivity information of neighboring voxels. Following conversion of the 3-D digital image to a skeletonized surface representation containing only one-dimensional (1-D) and two-dimensional (2-D) structures, each voxel is classified as a curve, surface, or junction. The method has been validated by means of synthesized images and has subsequently been applied to TB images from the human wrist. The topological parameters were found to predict Young's modulus (YM) for uniaxial loading, specifically, the surface-to-curve ratio was found to be the single strongest predictor of YM (r/sup 2/=0.69). Finally, the method has been applied to TB images from a group of patients showing very large variations in topological parameters that parallel much smaller changes in bone volume fraction (BVF).

近年来，成像技术的发展使得能够对松质骨（TB）的三维结构进行无损分析，松质骨形成由相互连接的板和杆组成的网络。大多数骨质疏松性骨折发生在富含TB的部位，这促使人们寻找作为骨强度决定因素的结构参数。在此，作者提出了一种新的方法...

In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

在医学图像分析应用中，大量标注数据的可用性变得越来越关键。然而，标注的医学数据往往稀缺且获取成本高昂。在本文中，我们通过应用基于对抗学习的最新技术来解决视网膜彩色图像合成的问题。在这种设置中，生成模型被训练以最大化由第二个模型提供的损失函数，该模型试图将其输出分类为真实或合成。特别地，我们提出实现一个对抗自编码器用于视网膜血管网络合成任务。我们将生成的血管树作为生成彩色视网膜图像的中间阶段，这是通过生成对抗网络完成的。两个模型都需要优化几乎处处可微的损失函数，这使得我们可以共同训练它们。最终模型提供了一个端到端的视网膜图像合成系统，能够通过从我们强加给相关潜在空间的简单概率分布进行采样，生成用户所需的任意数量的视网膜图像及其对应的血管网络。我们表明，学习到的潜在空间包含一个定义良好的语义结构，这意味着我们可以在视网膜图像空间中进行计算，例如，在两个视网膜图像之间平滑插值新的数据点。视觉和定量结果表明，合成的图像与训练集中的图像有实质性的不同，同时在解剖结构上保持一致，并显示出合理的视觉质量。

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which the authors track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C/sup 1/ continuous vector field is interpolated. The implementation proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

磁共振成像（MRI）具有独特的能力，能够非侵入性地、选择性地改变组织磁化，并在变形体（如心肌）内部创建标记模式。由此产生的模式定义了组织上随时间变化的曲线坐标系，作者使用耦合的B样条网格对其进行跟踪。B样条基提供了形状的局部控制、紧凑的表征...

In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

本文提出了一种新的方法，用于对比增强乳腺MRI的非刚性配准。作者开发了一种层次化的乳腺运动变换模型。乳腺的整体运动通过仿射变换建模，而局部运动则通过基于B样条的自由形变（FFD）来描述。使用了归一化互信息...

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

在二维超声中期妊娠检查中，识别和解释胎儿标准扫描切面是非常复杂的任务，需要多年的训练。除了引导探头到正确位置外，非专家同样难以识别图像中的相关结构。自动图像处理可以为经验丰富和缺乏经验的操作者提供工具来帮助完成这些任务。在本文中，我们提出了一种基于卷积神经网络的新方法，该方法可以在手持式二维超声数据中自动检测13个胎儿标准视图，并通过边界框提供胎儿结构的定位。一个重要的贡献是，网络仅基于图像级标签的弱监督学习来定位目标解剖结构。网络架构设计为实时运行，同时为定位任务提供最佳输出。我们展示了实时标注、从保存视频中回顾性帧检索以及在一个由完整临床异常筛查的图像和视频记录组成的非常大且具有挑战性的数据集上的定位结果。我们发现，在模拟实时检测的现实分类实验中，所提方法达到了0.798的平均F1分数，回顾性帧检索的准确率为90.09%。此外，定位任务的准确率达到77.8%。

A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

描述了一种用于校正磁共振（MR）数据中强度不均匀性的新方法，该方法无需组织类别模型即可实现高性能。其优势在于可在自动数据分析的早期阶段、在获得组织模型之前应用。该方法被称为非参数非均匀强度归一化（N3），...

This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses “pill-cam” technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.

本文描述了使用彩色图像分析在无线胶囊内窥镜（WCE）中自动区分食道、胃、小肠和结肠组织的方法。WCE使用“药丸相机”技术从整个胃肠道获取彩色视频图像。准确审查和报告这些数据是检查的重要部分，但繁琐且耗时。

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects-an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus.

用于医学图像分割的最新模型是U-Net和全卷积网络（FCN）的变体。尽管取得了成功，这些模型存在两个局限性：（1）它们的最优深度是先验未知的，需要大量的架构搜索或低效的集成不同深度的模型；（2）它们的跳跃连接施加了不必要的限制性融合方案，例...

A fast and simple algorithm for designing time-optimal waveforms is presented. The algorithm accepts a given arbitrary multidimensional $k$-space trajectory as the input and outputs the time-optimal gradient waveform that traverses $k$-space along that path in minimum time. The algorithm is noniterative, and its run time is independent of the complexity of the curve, i.e., the number of switches ...

提出了一种快速简单的算法用于设计时间最优波形。该算法接受给定的任意多维$k$空间轨迹作为输入，并输出沿该路径以最小时间遍历$k$空间的时间最优梯度波形。该算法非迭代，其运行时间与曲线的复杂度无关，即与开关次数...

The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images. >

中文摘要翻译待生成

A technique for producing geometrically accurate magnetic resonance images (MRIs) with undistorted intensity in the face of high levels of static field inhomogeneity arising from either source is presented. The technique requires the acquisition of two images of the same object with altered gradients. On the basis of a knowledge of these gradients it employs an automatic postprocessing step that exploits some invariant characteristics of the distortions to produce a rectified image from the two acquired images. No phantom imaging is involved and no operator interaction is required. The technique is theoretically justified and compared to other techniques, and experimental results that show that the technique works are presented. The improved accuracy in geometry and intensity may improve reliability of stereotactic surgery, may enhance the feasibility of both clinical and industrial imaging via external fields, and may increase the resolution of microscopic imaging. >

中文摘要翻译待生成

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

医学图像配准是医学图像处理中的一项重要任务。它指的是对齐数据集的过程，这些数据集可能来自不同的模态（例如，磁共振和计算机断层扫描）、不同的时间点（例如，随访扫描）和/或不同的受试者（在群体研究的情况下）。文献中描述了大量的图像配准方法。

During the last few years, optical coherence tomography (OCT) has demonstrated considerable promise as a method of high-resolution intravascular imaging. The goal of this study was to apply and to test the applicability of the rotating kernel transformation (RKT) technique to the speckle reduction and enhancement of OCT images. The technique is locally adaptive. It is based on sequential applicati...

在过去几年中，光学相干断层成像（OCT）已展现出作为高分辨率血管内成像方法的巨大潜力。本研究旨在应用并测试旋转核变换（RKT）技术对OCT图像进行散斑抑制和增强的适用性。该技术具有局部自适应性，基于连续应用...

Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density λ = λ(x, y, z) generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate λ of λ which maximizes the probability p(n*|λ) of observing the actual detector count data n* over all possible densities λ. Let independent Poisson variables n(b) with unknown means λ(b), b = 1, ···, B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ···, D with p(b, d) a one-step transition matrix, assumed known. We observe the total number n* = n*(d) of emissions in each detector unit d and want to estimate the unknown λ = λ(b), b = 1, ···, B. For each λ, the observed data n* has probability or likelihood p(n*|λ). The EM algorithm of mathematical statistics starts with an initial estimate λ0 and gives the following simple iterative procedure for obtaining a new estimate λnew, from an old estimate λold, to obtain Σk, k = 1, 2, ···, λnew(b)= λold(b) λDd=1 n*(d)p(b,d)/Σλold(bΣ)p(bλ,d),b=1,···B.

先前的发射断层扫描（ET）模型未能区分ET与透射断层扫描的物理原理。我们提出了一个更精确的ET通用数学模型，其中未知的发射密度λ = λ(x, y, z)产生待重建的图像，并从每个探测器单元d的计数n*(d)中重建。在该模型内，我们给出了一种确定λ估计的算法...