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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。

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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An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then op...

一种算法被开发出来，基于遗传算法（GA）优化边缘配置，用于检测医学图像中定位良好、无碎片、细小的边缘。添加了几项增强功能以提高算法相对于传统GA的性能。边缘图被分割成连通的子区域以减少解空间并简化问题。然后对边缘图进行...

Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of ${n}$ stacked transformations are applied to each image during network training. The underlying assumption is that the “expected” domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented “big” data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with ${n}={9}$ transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than “shallower” stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.

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Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-cl...

在皮肤镜图像中自动进行皮肤病变分类是提高诊断性能和减少黑色素瘤死亡的重要方法。尽管深度卷积神经网络（DCNN）在许多图像分类任务中取得了突破性进展，但由于训练数据不足、类间相似性和类内差异等原因，皮肤病变的准确分类仍然具有挑战性。

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

肝癌是导致癌症死亡的主要原因之一。为了辅助医生进行肝细胞癌的诊断和治疗规划，在临床实践中迫切需要一种准确且自动的肝脏和肿瘤分割方法。近年来，全卷积神经网络（FCNs），包括2D和3D FCNs，已成为许多体积图像分割的基础。然而，2D卷积无法充分利用第三维的空间信息，而3D卷积则面临高计算成本和GPU内存消耗的问题。为了解决这些问题，我们提出了一种新颖的混合密集连接UNet（H-DenseUNet），它由一个用于高效提取切片内特征的2D DenseUNet和一个用于在自动上下文算法精神下分层聚合体积上下文的3D DenseUNet组成，用于肝脏和肿瘤分割。我们以端到端的方式制定了H-DenseUNet的学习过程，其中切片内表示和切片间特征可以通过混合特征融合层联合优化。我们在MICCAI 2017肝脏肿瘤分割挑战赛数据集和3DIRCADb数据集上广泛评估了我们的方法。我们的方法在肿瘤分割结果上优于其他最先进的方法，并且即使使用单一模型，在肝脏分割上也取得了非常有竞争力的表现。

This article presents a detailed description of an algorithm for the automatic detection of the mid-sagittal plane in three-dimensional (3-D) brain images. The algorithm seeks the plane with respect to which the image exhibits maximum symmetry. For a given plane, symmetry is measured by the cross-correlation between the image sections lying on either side. The search for the plane of maximum symme...

本文详细介绍了一种用于三维脑图像中矢状面自动检测的算法。该算法寻找使图像表现出最大对称性的平面。对于给定平面，对称性通过两侧图像区域之间的互相关来测量。寻找最大对称性平面的搜索...

Monte Carlo (MC) methods can accurately simulate scatter in X-ray imaging. However, when low noise scatter projections have to be simulated these MC simulations tend to be very time consuming. Rapid computation of scatter estimates is essential for several applications. The aim of the work presented in this paper is to speed up the estimation of noise-free scatter projections while maintaining the...

蒙特卡洛（MC）方法能够准确模拟X射线成像中的散射。然而，当需要模拟低噪声散射投影时，这些MC模拟往往非常耗时。快速计算散射估计对于多种应用至关重要。本文旨在加速无噪声散射投影的估计，同时保持...

Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.

描述了一种自动定位和勾勒眼底图像中血管的方法。该工具应有助于眼科专家进行患者筛查、治疗评估和临床研究。作者的方法与以往已知方法的不同之处在于，它协同使用局部和全局血管特征来分割血管网络。作者使用20幅图像的手动标记真值分割来评估其方法。操作特性曲线显示，在高达75%的真阳性率下，与匹配滤波器响应（MFR）的基本阈值处理相比，作者的方法将假阳性降低了多达15倍。作为基线，他们还将真值与第二次手动标记进行了比较，平均得到90%的真阳性和4%的假阳性检测率。这些数字表明，在相同的假阳性率下，作者的方法仍有15%的真阳性率提升空间。他们公开了所有图像和手动标记，供感兴趣的研究人员用于评估相关方法。

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-a...

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We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph .

我们提出VoxelMorph，一种基于学习的快速可变形成对医学图像配准框架。传统配准方法为每对图像优化目标函数，对于大数据集或丰富形变模型可能耗时。相比之下，基于近期学习方法，我们将配准公式化为一个函数，将输入图像对映射到对齐这些图像的形变场。我们通过卷积神经网络参数化该函数，并在图像集上优化网络参数。给定一对新扫描，VoxelMorph通过直接评估函数快速计算形变场。本文探索两种不同训练策略。第一种（无监督）设置中，我们训练模型最大化基于图像强度的标准图像匹配目标函数。第二种设置中，我们利用训练数据中的辅助分割。我们证明无监督模型的准确性与最新方法相当，而速度快数个数量级。我们还显示，使用辅助数据训练的VoxelMorph在测试时提高配准精度，并评估训练集大小对配准的影响。我们的方法有望加速医学图像分析和处理流程，同时促进基于学习的配准及其应用的新方向。我们的代码在https://github.com/voxelmorph/voxelmorph免费提供。

Most of ultrasound medical imaging systems currently on the market implement standard Delay and Sum (DAS) beamforming to form B-mode images. However, image resolution and contrast achievable with DAS are limited by the aperture size and by the operating frequency. For this reason, different beamformers have been presented in the literature that are mainly based on adaptive algorithms, which allow achieving higher performance at the cost of an increased computational complexity. In this paper, we propose the use of an alternative nonlinear beamforming algorithm for medical ultrasound imaging, which is called Delay Multiply and Sum (DMAS) and that was originally conceived for a RADAR microwave system for breast cancer detection. We modify the DMAS beamformer and test its performance on both simulated and experimentally collected linear-scan data, by comparing the Point Spread Functions, beampatterns, synthetic phantom and in vivo carotid artery images obtained with standard DAS and with the proposed algorithm. Results show that the DMAS beamformer outperforms DAS in both simulated and experimental trials and that the main improvement brought about by this new method is a significantly higher contrast resolution (i.e., narrower main lobe and lower side lobes), which turns out into an increased dynamic range and better quality of B-mode images.

目前市场上大多数超声医学成像系统采用标准的延迟与和（DAS）波束形成来生成B模式图像。然而，DAS的图像分辨率和对比度受限于孔径大小和工作频率。因此，文献中提出了不同的波束形成器，主要基于自适应算法，这些算法以增加计算复杂度为代价实现了更高的性能。在本文中，我们提出将一种替代的非线性波束形成算法用于医学超声成像，该算法称为延迟相乘与和（DMAS），最初是为用于乳腺癌检测的雷达成像微波系统而设计的。我们修改了DMAS波束形成器，并通过比较使用标准DAS和所提算法获得的点扩散函数、波束图、合成体模和体内颈动脉图像，在模拟和实验采集的线性扫描数据上测试其性能。结果表明，DMAS波束形成器在模拟和实验试验中均优于DAS，并且这种新方法带来的主要改进是显著更高的对比度分辨率（即更窄的主瓣和更低的旁瓣），从而提高了动态范围和B模式图像的质量。

Images captured with a typical endoscope show spatial distortion, which necessitates distortion correction for subsequent analysis. Here, a new methodology based on least squares estimation is proposed to correct the nonlinear distortion in the endoscopic images. A mathematical model based on polynomial mapping is used to map the images from distorted image space onto the corrected image space. Th...

使用典型内窥镜捕获的图像存在空间畸变，这需要畸变校正以便后续分析。本文提出了一种基于最小二乘估计的新方法来校正内窥镜图像中的非线性畸变。采用基于多项式映射的数学模型将图像从畸变图像空间映射到校正后的图像空间。

A new method is proposed to subtract the count of scattered photons from that acquired with a photopeak window at each pixel in each planar image of single-photon emission computed tomography (SPECT). The subtraction is carried out using two sets of data: one set is acquired with a main window centered at photopeak energy and the other is acquired with two subwindows on both sides of the main window. The scattered photons included in the main window are estimated from the counts acquired with the subwindows and then they are subtracted from the count acquired with the main windows. Since the subtraction is performed at each pixel in each planar image, the proposed method has the potential to be more precise than conventional methods. For three different activity distributions in cylinder phantoms, simulation tests gave good agreement between the activity distributions reconstructed from unscattered photons and those from the corrected data. >

中文摘要翻译待生成

Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.

表征图像分割方法的性能一直是一个持续的挑战。性能分析很重要，因为分割算法通常精度和准确度有限。由人类评分员交互式绘制所需分割通常曾是唯一可接受的方法，但存在评分员内和评分员间的变异性。为了消除评分员引入的变异性，人们寻求自动化算法，但必须评估这些算法以确保它们适合任务。生成医学图像分割的评分员（人类或算法）的性能难以量化，因为难以获得或估计临床数据的已知真实分割。尽管可以构建物理和数字幻影，其真实值已知或易于估计，但由于难以构建再现临床数据中观察到的全部成像特征及正常和病理解剖变异性的幻影，此类幻影不能完全反映临床图像。与评分员的分割集合进行比较是一种有吸引力的替代方案，因为它可以直接在相关临床成像数据上进行。然而，用于比较这些分割的最合适的度量或度量集尚未明确，实际中使用了多种度量。我们在此提出一种用于同时真实值及性能水平估计的期望最大化算法（STAPLE）。该算法考虑一个分割集合，计算真实分割的概率估计以及每个分割所代表的性能水平的度量。集合中每个分割的来源可以是经过适当训练的人类评分员或一个自动化分割算法。通过估计分割的最优组合，根据估计的性能水平对每个分割进行加权，并结合被分割结构的空间分布先验模型以及空间同质性约束，形成真实分割的概率估计。STAPLE易于应用于临床成像数据，能够方便地评估自动化图像分割算法的性能，并实现人类评分员与算法性能的直接比较。

The notion of a linogram is introduced. It corresponds to the notion of a sinogram in the conventional representation of projection data in image reconstruction. In the sinogram, points which correspond to rays which go through a fixed point in the cross section to be reconstructed all fall on a sinusoidal curve. In the linogram, however, these points fall on a straight line. Thus, backprojection ...

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The authors address the problem of applying spatial transformations (or "image warps") to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. The authors present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how their methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of the authors' methods improve the consistency between registered and target images over naive warping algorithms.

作者解决了将空间变换（或“图像扭曲”）应用于扩散张量磁共振图像的问题。这些图像包含的方向信息在图像配准过程中进行空间变换时必须得到适当处理。作者提出了三维图像全局变换的解决方案，达到12参数仿射复杂度，并说明了如何将其方法扩展到更高阶变换。提出了几种方法，并使用合成数据进行了测试。其中一种方法，即主方向保留算法，考虑了剪切、拉伸和刚体旋转，被证明是最有效的。还对从单个受试者获得的人脑数据进行了额外的配准实验，该受试者的头部在扫描仪内以三种不同方向成像。作者的所有方法都比简单的扭曲算法提高了配准图像和目标图像之间的一致性。

This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture...

本文提出了一种用于单能量和双能量计算机断层扫描（CT）成像的新型重建算法。通过将X射线束的多色特性纳入重建过程，该算法能够消除射束硬化伪影。该算法的单能量版本假设扫描场中的每个体素可以表示为混合物...

Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

鉴于X射线辐射对患者的潜在风险，低剂量CT在医学成像领域引起了相当大的关注。目前，主流的低剂量CT方法包括设备特定的正弦图域滤波和迭代重建算法，但它们需要访问原始数据，而这些数据的格式对大多数用户不透明。由于在图像域中建模统计特性的困难，现有直接处理重建图像的方法在保持结构细节的同时无法很好地消除图像噪声。受深度学习思想的启发，我们将自编码器、反卷积网络和快捷连接结合到残差编码器-解码器卷积神经网络（RED-CNN）中，用于低剂量CT成像。经过基于块的训练后，所提出的RED-CNN在模拟和临床案例中均达到了与最先进方法相竞争的性能。特别是，我们的方法在噪声抑制、结构保留和病变检测方面得到了良好的评价。

This paper proposes an adaptive image enhancement method for mammographic images, which is based on the first derivative and the local statistics. The adaptive enhancement method consists of three processing steps. The first step is to remove the film artifacts which may be misread as microcalcifications. The second step is to compute the gradient images by using the first derivative operators. Th...

本文提出了一种基于一阶导数和局部统计的自适应乳腺X线图像增强方法。该自适应增强方法包括三个处理步骤。第一步是去除可能被误读为微钙化的胶片伪影。第二步是利用一阶导数算子计算梯度图像。

Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel mis-classifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need the authors have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.

高精度的大脑皮层表面模型作为研究人脑功能组织的工具变得越来越重要。由于皮层高度折叠，使用当前的神经影像技术构建此类模型非常困难。即使单个体素的错误分类也可能导致相邻脑回之间产生错误连接，从而产生拓扑不准确的模型。这些拓扑缺陷使得皮层模型不再与曲面片同胚，从而阻止了重建皮层的精确膨胀、展平或球形变形。表面变形技术可以保证模型的拓扑正确性，但耗时且可能导致几何不准确的模型。为了解决这一需求，作者开发了一种技术，用于获取皮层模型，检测并修复拓扑缺陷，同时保留模型的大部分结构，从而得到既几何精确又拓扑正确的表面。

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each pati...

中文摘要翻译待生成

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and im...

压缩感知磁共振成像（CS-MRI）为加速耗时的MRI采集过程提供了理论基础。然而，它主要依赖于迭代数值求解器，这仍然阻碍了其在时间关键应用中的适应。此外，深度神经网络的最新进展显示了它们在计算机视觉和图像处理中的潜力。

After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfils all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, the authors present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).

在构思和实现任何新的医学图像处理算法之后，验证是一个重要步骤，以确保该过程满足初始设计阶段提出的所有要求。虽然算法必须在真实数据上进行评估，但全面的验证还需要额外使用模拟数据，因为无法从体内数据建立金标准...

Presents a model-based phase unwrapping method which represents the unwrapped phase function by a truncated Taylor series and a residual function. An efficient, noniterative computational algorithm is also proposed for calculating the model parameters from the phase derivatives. Sample experimental results are shown to demonstrate the effectiveness of the algorithm for extracting unwrapped phase i...

提出了一种基于模型的相位展开方法，该方法通过截断的泰勒级数和残差函数来表示展开的相位函数。还提出了一种高效的非迭代计算算法，用于从相位导数计算模型参数。示例实验结果表明了该算法在提取展开相位方面的有效性……