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Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

在皮肤镜图像中自动分割皮肤病变是一项具有挑战性的任务，因为病变与周围皮肤之间的对比度低，病变边界不规则且模糊，存在各种伪影以及不同的成像采集条件。本文提出了一种全自动的皮肤病变分割方法，利用19层深度卷积神经网络进行端到端训练，无需依赖数据的先验知识。我们提出了一系列策略，以确保在有限的训练数据下实现有效且高效的学习。此外，我们设计了一种基于Jaccard距离的新型损失函数，以消除样本重新加权的需要——在使用交叉熵作为图像分割损失函数时，由于前景和背景像素数量严重不平衡，通常需要进行样本重新加权。我们在两个公开数据库上评估了所提框架的有效性、效率以及泛化能力。一个来自ISBI 2016皮肤病变分析挑战赛（针对黑色素瘤检测），另一个是PH2数据库。实验结果表明，所提方法在这两个数据库上优于其他最先进的算法。我们的方法足够通用，只需要最少的预处理和后处理，因此可以应用于各种医学图像分割任务。

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentatio...

乳腺X线摄影风险评分通常通过从乳腺X线照片中提取一组手工特征，并将响应直接或间接与乳腺癌风险相关联来实现自动化。我们提出了一种从未标记数据中学习特征层次结构的方法。当学习到的特征被用作简单分类器的输入时，可以解决两个不同的任务：i）乳腺密度分割...

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...

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimate...

本文提出一种全自动算法，用于从多光谱磁共振（MR）图像中分割多发性硬化（MS）病灶。该方法利用正常脑图像的随机模型进行基于强度的组织分类，同时将模型中无法很好解释的异常点检测为MS病灶。它校正了MR场不均匀性，并估计...

A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are mo...

提出了一种统计模型，用于表示单通道磁共振（MR）脑图像中主要组织类别的分布。利用该模型，将脑图像分割为灰质、白质和脑脊液（CSF）。该模型考虑了随机噪声、磁场不均匀性和组织的生物学变异。强度测量被...

The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.

美国国家医学图书馆（NLM）正在开发一种数字胸部X光（CXR）筛查系统，用于资源受限的社区和发展中国家，重点关注结核病的早期检测。在数字CXR的计算机辅助诊断中，自动检测肺部区域是一个关键组成部分。本文提出了一种非刚性配准方法...

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

传统的胸部X线照片在临床实践中仍然无处不在，并且很可能在相当长的一段时间内保持这种状态。然而，其解读是出了名的困难。这解释了对计算机辅助诊断在胸部X线摄影中持续的兴趣。本综述的目的是对关于胸部图像计算机分析的文献进行分类和简要回顾，这包括过去30年发表的150多篇论文。指出了剩余的挑战，并给出了一些未来研究的方向。

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...

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.

中文摘要翻译待生成

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...

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

本文描述了一种针对X射线计算机断层扫描（CT）的统计图像重建方法，该方法基于物理模型，考虑了多能X射线源光谱以及由能量依赖衰减引起的测量非线性。我们假设物体由给定数量的非重叠材料组成，例如软组织和骨骼。每个体素的衰减系数是其未知密度与已知能量依赖质量衰减系数的乘积。我们为此多能模型制定了惩罚似然函数，并开发了一种有序子集迭代算法来估计每个体素中的未知密度。当使用一个子集时，该算法在每次迭代中单调地降低成本函数。将此方法应用于包含骨骼和软组织的物体的模拟X射线CT测量，生成的图像显著减少了射束硬化伪影。

Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.

脑微出血是血管附近的小出血。它们已被认为是许多脑血管疾病和认知功能障碍的重要诊断生物标志物。在当前的临床常规中，脑微出血由放射科医生手动标记，但这一过程费力、耗时且容易出错。在本文中，我们提出了一种新颖的自动检测脑微出血的方法……

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...

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。

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...

With the increased interest in computer-aided image analysis methods, there is a greater need for objective methods of algorithm evaluation. Validation of in vivo MRI studies is complicated by a lack of reference data and the difficulty of constructing anatomically realistic physical phantoms. The authors present here an extensible MRI simulator that efficiently generates realistic three-dimensional (3-D) brain images using a hybrid Bloch equation and tissue template simulation that accounts for image contrast, partial volume, and noise. This allows image analysis methods to be evaluated with controlled degradations of image data.

随着对计算机辅助图像分析方法的兴趣增加，对算法评估的客观方法的需求也更大。体内MRI研究的验证因缺乏参考数据和构建解剖学逼真的物理体模的困难而变得复杂。作者在此介绍一种可扩展的MRI模拟器，能够高效生成逼真的三维...

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。

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。

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 watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.

分水岭变换具有有趣的特性，使其在许多不同的图像分割应用中非常有用：它简单直观，可以并行化，并且总是产生图像的完整分割。然而，当应用于医学图像分析时，它具有重要的缺点（过度分割、对噪声敏感、对薄结构或低信噪比结构的检测能力差...）

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。

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional neural network (CNN)-based segmentation methods with the goal of reducing HD directly. We propose three methods to estimate HD from the segmentation probability map produced by a CNN. One method makes use of the distance transform of the segmentation boundary. Another method is based on applying morphological erosion on the difference between the true and estimated segmentation maps. The third method works by applying circular/spherical convolution kernels of different radii on the segmentation probability maps. Based on these three methods for estimating HD, we suggest three loss functions that can be used for training to reduce HD. We use these loss functions to train CNNs for segmentation of the prostate, liver, and pancreas in ultrasound, magnetic resonance, and computed tomography images and compare the results with commonly-used loss functions. Our results show that the proposed loss functions can lead to approximately 18-45% reduction in HD without degrading other segmentation performance criteria such as the Dice similarity coefficient. The proposed loss functions can be used for training medical image segmentation methods in order to reduce the large segmentation errors.

中文摘要翻译待生成

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...

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。