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We developed a general method for global conformal parameterizations based on the structure of the cohomology group of holomorphic one-forms for surfaces with or without boundaries (Gu and Yau, 2002), (Gu and Yau, 2003). For genus zero surfaces, our algorithm can find a unique mapping between any two genus zero manifolds by minimizing the harmonic energy of the map. In this paper, we apply the alg...

我们基于有界或无界曲面的全纯一次微分形式的同调群结构，发展了一种全局共形参数化的通用方法（Gu and Yau, 2002; Gu and Yau, 2003）。对于零亏格曲面，我们的算法可以通过最小化映射的调和能量，在任意两个零亏格流形之间找到唯一映射。在本文中，我们将该算法应用于...

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” Thi...

在过去几年中，机器学习（或更广义的人工智能）引发了压倒性的研究兴趣，并吸引了前所未有的公众关注。作为断层成像研究人员，我们从成像角度分享了这一兴奋（见附录第1项），并组织了这期特刊，专注于“机器学习在图像重建中的应用”主题。本文...

Single photon emission computed tomography (SPECT) imaging with /sup 201/Tl or /sup 99m/Tc agent is used to assess the location or the extent of myocardial infarction or ischemia. A method is proposed to decrease the effect of operator variability in the visual or quantitative interpretation of scintigraphic myocardial perfusion studies. To effect this, the patient's myocardial images (target case...

使用/sup 201/Tl或/sup 99m/Tc试剂的单光子发射计算机断层扫描(SPECT)成像用于评估心肌梗死或缺血的位置或程度。提出了一种方法以减少操作员变异性在闪烁心肌灌注研究的视觉或定量解释中的影响。为了实现这一点，患者的心肌图像(目标病例...)

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional ne...

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This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classif...

本文提出了一种自动方法用于视网膜图像中血管网络的分割。该算法从提取血管中心线开始，这些中心线作为后续血管填充阶段的指导。为此，处理四个方向微分算子的输出以选择连接的候选点集，以便进一步分类...

The structure of microvasculature cannot be resolved using standard clinical ultrasound (US) imaging frequencies due to the fundamental diffraction limit of US waves. In this work, we use a standard clinical US system to perform in vivo sub-diffraction imaging on a CD1, female mouse aged eight weeks by localizing isolated US signals from microbubbles flowing within the ear microvasculature, and compare our results to optical microscopy. Furthermore, we develop a new technique to map blood velocity at super-resolution by tracking individual bubbles through the vasculature. Resolution is improved from a measured lateral and axial resolution of 112 μm and 94 μm respectively in original US data, to super-resolved images of microvasculature where vessel features as fine as 19 μm are clearly visualized. Velocity maps clearly distinguish opposing flow direction and separated speed distributions in adjacent vessels, thereby enabling further differentiation between vessels otherwise not spatially separated in the image. This technique overcomes the diffraction limit to provide a noninvasive means of imaging the microvasculature at super-resolution, to depths of many centimeters. In the future, this method could noninvasively image pathological or therapeutic changes in the microvasculature at centimeter depths in vivo.

由于超声波的固有衍射极限，标准临床超声成像频率无法解析微血管结构。在本工作中，我们使用标准临床超声系统，通过定位来自流经耳微血管的微泡的孤立超声信号，对一只八周龄的CD1雌性小鼠进行体内亚衍射成像，并将我们的结果与光学显微镜进行比较。此外，我们开发了一种新技术，通过追踪血管中的单个气泡来绘制超分辨血流速度。分辨率从原始超声数据中测量的横向112 μm和轴向94 μm提高到微血管超分辨图像，其中可清晰观察到细至19 μm的血管特征。速度图清晰区分相邻血管中的相反流动方向和分离的速度分布，从而进一步区分图像中原本未空间分离的血管。该技术突破了衍射极限，提供了一种非侵入性的超分辨微血管成像方法，深度可达数厘米。未来，该方法可无创地成像体内厘米深度处微血管的病理或治疗变化。

This paper deals with an automated method for the simultaneous correction of patient motion and gray values prior to subtraction in digitized angiography. The algorithm consists of maximizing the deterministic sign change (DSC) criterion with respect to three registration parameters (two translational shifts and one constant value added to the pixel values of the image with contrast medium). This ...

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Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses ...

在MR脑图像中进行自动分割对于涉及所有年龄段图像的大规模研究中的定量分析非常重要。本文提出了一种使用卷积神经网络将MR脑图像自动分割成多个组织类别的方法。为了确保该方法获得准确的分割细节以及空间一致性，网络使用了...

Presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. The authors assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further they assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. The authors present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

提出了一种新的校正磁共振成像（MRI）中强度不均匀性的方法，该方法显著改善了基于强度的组织分割。图像亮度值由于低频偏置场而失真，妨碍了视觉检查和分割。这种新的校正方法称为参数化偏置场校正（PABIC），基于一个简化模型……

Magnetic particle imaging allows to determine the spatial distribution of magnetic nanoparticles in vivo. The system matrix in magnetic particle imaging is commonly acquired in a tedious calibration scan and requires to measure the system response at numerous positions in the field-of-view. In this paper, we propose a method that significantly reduces the number of required calibration scans. It e...

磁粒子成像能够确定体内磁性纳米颗粒的空间分布。磁粒子成像中的系统矩阵通常通过繁琐的校准扫描获取，需要在视场中的多个位置测量系统响应。在本文中，我们提出了一种显著减少所需校准扫描次数的方法。它...

Lossless compression techniques are essential in archival and communication of medical images. Here, a new segmentation-based lossless image coding (SLIC) method is proposed, which is based on a simple but efficient region growing procedure. The embedded region growing procedure produces an adaptive scanning pattern for the image with the help of a very-few-bits-needed discontinuity index map. Alo...

无损压缩技术对于医学图像的存档和通信至关重要。本文提出了一种新的基于分割的无损图像编码（SLIC）方法，该方法基于一个简单但高效的区域生长过程。嵌入的区域生长过程借助一个只需极少比特的不连续性指标图，为图像生成自适应扫描模式。

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

自动化核检测是许多计算机辅助病理相关图像分析算法（如乳腺癌组织标本的自动分级）的关键步骤。诺丁汉组织学评分系统与组织病理学图像中乳腺癌细胞核的形状和外观高度相关。然而，自动核检测面临两个挑战：1) 大量细胞核和高分辨率数字化病理图像的尺寸；2) 单个细胞核在大小、形状、外观和纹理上的变异性。近年来，“深度学习”策略在大图像数据的分类和分析中引起了关注。组织病理学由于其规模和复杂性，是应用深度学习策略的绝佳案例。本文提出了一种堆叠稀疏自动编码器（SSAE），作为一种深度学习策略的实例，用于在乳腺癌的高分辨率组织病理学图像上进行高效的细胞核检测。SSAE仅从像素强度中学习高层特征，以识别细胞核的区分性特征。对每幅图像应用滑动窗口操作，通过自编码器获取的高层特征表示图像块，然后将其输入分类器，将每个图像块分类为核或非核。在500张组织病理学图像（2200×2200）和约3500个手动分割的单个细胞核作为金标准的队列中，SSAE显示出改进的F-measure为84.49%，精确率-召回率曲线下的平均面积（AveP）为78.83%。SSAE方法还优于其他九种最先进的核检测策略。

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. Following recent developments in compressive sensing (CS) theory, several authors have demonstrated that certain classes of MR images which possess sparse representations in some transform domain can be accurately reconstructed from very highly undersampled K -space data by solving a convex lscr 1 -minimization problem. Although lscr 1 -based techniques are extremely powerful, they inherently require a degree of over-sampling above the theoretical minimum sampling rate to guarantee that exact reconstruction can be achieved. In this paper, we propose a generalization of the CS paradigm based on homotopic approximation of the lscr 0 quasi-norm and show how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound. Following a brief review of standard CS methods and the developed theoretical extensions, several example MRI reconstructions from highly undersampled K -space data are presented.

在临床磁共振成像（MRI）中，扫描时间的任何减少都能带来诸多潜在好处，从高时间速率观察生理过程到提高患者舒适度。随着压缩感知（CS）理论的最新发展，多位作者已经证明，通过求解一个凸的ℓ1最小化问题，可以从高度欠采样的K空间数据中准确重建出在某些变换域中具有稀疏表示的特定类别的MR图像。尽管基于ℓ1的技术非常强大，但它们本质上需要高于理论最小采样率的过采样程度，以确保能够实现精确重建。在本文中，我们提出了一种基于ℓ0拟范数同伦近似的CS范式的推广，并展示了如何将MR图像重建进一步推至奈奎斯特极限以下，并显著接近理论界限。在简要回顾标准CS方法和所发展的理论扩展之后，我们展示了几个从高度欠采样的K空间数据进行MRI重建的示例。

The authors propose a new 2-D point source scattering deconvolution method. The cross-plane scattering is incorporated into the algorithm by modeling a scattering point source function. In the model, the scattering dependence on axial and transaxial directions is reflected in the exponential fitting parameters, and these parameters are directly estimated from a limited number of measured point res...

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Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained ...

稀疏视角计算机断层扫描（CT）在加速数据采集和减少CT扫描辐射剂量方面具有很大前景。最近稀疏视角CT重建算法的进展，如迭代重建算法，在获得高质量图像的同时需要先进的计算能力。近来，深度学习（DL）已广泛应用于各种应用，并取得了...

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address the...

卷积神经网络（CNN）在自动医学图像分割方面取得了最先进的性能。然而，它们尚未在临床应用中展现出足够准确和稳健的结果。此外，它们还受限于缺乏图像特定的适应性以及对先前未见过的对象类别（即零样本学习）缺乏泛化能力。为了解决这些问题...

This paper addresses the long object problem in helical cone-beam computed tomography. The authors present the PHI-method, a new algorithm for the exact reconstruction of a region-of-interest (ROI) of a long object from axially truncated data extending only slightly beyond the ROI. The PHI-method is an extension of the Radon-method, published by Kudo et al. in Phys. in Med. and Biol., vol. 43, p. ...

本文解决了螺旋锥束计算机断层扫描中的长物体问题。作者提出了PHI方法，这是一种新算法，用于从仅略超出感兴趣区域（ROI）的轴向截断数据中精确重建长物体的感兴趣区域。PHI方法是Radon方法的扩展，由Kudo等人在《Phys. in Med. and Biol.》第43卷中发表。

An overview of the Shinnar-Le Roux (SLR) algorithm is presented. It is shown how the performance of SLR pulses can be very accurately specified analytically. This reveals how to design a pulse that produces a specified slice profile and allows the pulse designer to trade off analytically the parameters describing the pulse performance. Several examples are presented to illustrate the more importan...

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Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation ...

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Computerized analysis of the tongue surface movement can provide valuable information to speech and swallowing research. Ultrasound technology is currently the most attractive modality for the tongue imaging mainly because of its high video frame rate. However, problems with ultrasound imaging, such as noise and echo artifacts, refractions, and unrelated reflections pose significant challenges for...

舌头表面运动的计算机化分析可以为言语和吞咽研究提供有价值的信息。超声技术目前是舌头成像最具吸引力的模态，主要因为其高视频帧率。然而，超声成像的问题，如噪声和回声伪影、折射以及无关反射，给...带来了重大挑战。

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.

医学图像采集设备提供了大量的解剖和功能信息，这些信息在定量图像分析方法的支持下，便利并改善了诊断和患者治疗。然而，特定模态的图像伪影，例如磁共振图像（MRI）中的强度不均匀性现象，仍然显著，并可能对定量图像分析产生不利影响。本文回顾了已开发的众多用于减少或消除MRI强度不均匀性的方法。首先，根据不均匀性校正策略对方法进行分类。其次，回顾了不同的定性和定量评估方法。第三，根据若干特征对60篇相关出版物进行分类和分析，以揭示主要趋势、流行程度、评估策略和应用。最后，在分析结果的支持下，讨论了不均匀性校正领域的关键评估问题和未来发展。

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

我们提出了一种用于医学图像噪声过滤的鲁棒小波域方法。该方法能够自适应地处理各种类型的图像噪声，并满足医学专家的偏好；通过单个参数，可以在保留（依赖于专家的）相关细节和降噪程度之间取得平衡。该算法利用关于显著图像特征在不同分辨率尺度上相关性的普遍有效知识，进行初步的系数分类。这种初步的系数分类用于经验性地估计代表有用图像特征的系数和主要代表噪声的系数的统计分布。通过使用局部空间活动的小波域指示器来实现对图像空间上下文的适应。该方法在实现和执行时间上都具有较低的复杂度。结果证明了它在医学超声和磁共振成像中噪声抑制的有效性。在这些应用中，无论在定量性能指标还是图像视觉质量方面，该方法都明显优于单分辨率空间自适应算法。

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer’s ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

从二维超声心动图像中勾画心脏结构是临床诊断中常见任务。过去几十年，该任务的自动化一直是研究热点。本文评估了最先进的编码器-解码器深度卷积神经网络方法在二维超声心动图像评估上的表现，即分割心脏结构和估计临床指标，使用一个专门为此目标设计的数据集。因此，我们引入了心脏多结构超声分割数据集，这是目前最大且完全标注的公开数据集，用于超声心动评估。该数据集包含来自500名患者的双腔和四腔采集，由一位心脏病专家对整个数据集进行参考测量，并由三位心脏病专家对50名患者的子集进行测量。结果表明，基于编码器-解码器的架构优于最先进的非深度学习方法，并忠实再现了专家对左心室舒张末期和收缩末期容积的分析，平均相关性达0.95，绝对平均误差为9.5毫升。关于左心室的射血分数，结果较为对比，平均相关系数为0.80，绝对平均误差为5.6%。尽管这些结果低于观察者间评分，但略优于观察者内评分。基于这一观察，定义了改进方向，为二维超声心动图像的准确和全自动分析打开了大门。

The authors analyzed the noise characteristics of two-dimensional (2-D) and three-dimensional (3-D) images obtained from the GE Advance positron emission tomography (PET) scanner. Three phantoms were used: a uniform 20-cm phantom, a 3-D Hoffman brain phantom, and a chest phantom with heart and lung inserts. Using gated acquisition, the authors acquired 20 statistically equivalent scans of each pha...

作者分析了从GE Advance正电子发射断层扫描（PET）扫描仪获得的二维（2-D）和三维（3-D）图像的噪声特性。使用了三个体模：一个均匀的20厘米体模，一个3-D Hoffman脑体模，以及一个带有心脏和肺部插入物的胸部体模。使用门控采集，作者获得了每个相位的20个统计上等效的扫描...

We employ the new geometric active contour models, previously formulated, for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a p...

我们采用先前提出的新型几何主动轮廓模型，用于磁共振成像（MRI）、计算机断层扫描（CT）和超声医学图像的边缘检测与分割。我们的方法基于在给定图像上定义基于特征的度量，这进而导致一种新的蛇模型范式，其中感兴趣的特征可以被视为位于一个...的底部。