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The current trend in attenuation correction for single photon emission computed tomography (SPECT) is to measure and reconstruct the attenuation coefficient map using a transmission scan, performed either sequentially or simultaneously with the emission scan. This approach requires dedicated hardware and increases the cost (and in some cases the scanning time) required to produce a clinical SPECT ...

当前单光子发射计算机断层扫描（SPECT）衰减校正的趋势是使用透射扫描测量和重建衰减系数图，该扫描可与发射扫描顺序或同时进行。这种方法需要专用硬件，并增加了临床SPECT...

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

我们提出了一种新颖的模糊分割算法，用于磁共振成像（MRI）数据的模糊分割和强度不均匀性估计。MRI强度不均匀性可归因于射频线圈的缺陷或与采集序列相关的问题。结果是在图像上产生缓慢变化的阴影伪影，这可能导致基于强度的传统分类产生误差。我们的算法通过修改标准模糊C均值（FCM）算法的目标函数来补偿这种不均匀性，并允许像素（体素）的标记受其邻近邻域标记的影响。邻域效应作为正则化器，使解偏向于分段均匀的标记。这种正则化对于分割受椒盐噪声污染的扫描图像非常有用。给出了合成图像和MR数据的实验结果，以证明所提算法的有效性和效率。

Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artifacts. Traditionally, such observation led to a formulation of an optimization problem, which was solved using iterative algorithms. Recently, however, deep learning-based approaches have gained significant popularity due to their ability to solve general inverse problems. In this paper, we propose a unique, novel convolutional recurrent neural network architecture which reconstructs high quality cardiac MR images from highly undersampled k-space data by jointly exploiting the dependencies of the temporal sequences as well as the iterative nature of the traditional optimization algorithms. In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modeling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations. In addition, spatio–temporal dependencies are simultaneously learnt by exploiting bidirectional recurrent hidden connections across time sequences. The proposed method is able to learn both the temporal dependence and the iterative reconstruction process effectively with only a very small number of parameters, while outperforming current MR reconstruction methods in terms of reconstruction accuracy and speed.

加速动态磁共振成像的数据采集导致了一个具有挑战性的病态逆问题，这在过去几十年中引起了信号处理和机器学习社区的极大兴趣。该问题的关键是如何利用MR序列的时间相关性来消除混叠伪影。传统上，这种观察导致了优化问题的公式化，并通过迭代算法求解。然而，近年来，基于深度学习的方法因其解决一般逆问题的能力而获得了显著的普及。在本文中，我们提出了一种独特的、新颖的卷积递归神经网络架构，该架构通过联合利用时间序列的依赖性和传统优化算法的迭代性质，从高度欠采样的k空间数据中重建高质量的心脏MR图像。特别地，所提出的架构嵌入了传统迭代算法的结构，通过在这些迭代中使用递归隐藏连接有效地模拟了迭代重建阶段的循环性。此外，通过利用跨时间序列的双向递归隐藏连接同时学习时空依赖性。所提出的方法能够以非常少的参数有效学习时间依赖性和迭代重建过程，同时在重建精度和速度方面优于当前的MR重建方法。

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive p...

X射线计算机断层扫描（CT）采用稀疏投影视角是一种降低辐射剂量的近期方法。然而，由于投影视角不足，使用滤波反投影（FBP）的分析重建方法会产生严重的条纹伪影。最近，使用大感受野神经网络（如U-Net）的深度学习方法已展现出令人印象深刻的...

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

计算机断层扫描（CT）在医疗实践中的不断发展和广泛应用引起了公众对患者所受辐射剂量的关注。降低辐射剂量可能导致噪声和伪影增加，进而影响放射科医生的判断和信心。因此，需要从低剂量CT数据中进行先进的图像重建以提高诊断性能，但由于其不适定性，这是一个具有挑战性的问题。在过去几年中，各种低剂量CT方法取得了令人印象深刻的结果。然而，大多数针对此应用开发的算法，包括最近流行的深度学习技术，都旨在最小化去噪CT图像与真值之间的均方误差（MSE），并采用通用惩罚项。尽管峰值信噪比有所提高，但基于MSE或加权MSE的方法在积极去噪后可能会损害重要结构细节的可视性。本文介绍了一种基于生成对抗网络（GAN）的新型CT图像去噪方法，该方法结合了Wasserstein距离和感知相似性。Wasserstein距离是最优传输理论中的一个关键概念，有望提升GAN的性能。感知损失通过在已建立的特征空间中比较去噪输出与真值的感知特征来抑制噪声，而GAN则更多地关注在统计上将数据噪声分布从强迁移到弱。因此，我们提出的方法将视觉感知知识转移到图像去噪任务中，不仅能够降低图像噪声水平，同时还能尝试保留关键信息。在临床CT图像实验中取得了令人满意的结果。

Proliferative diabetic retinopathy is a rare condition likely to lead to severe visual impairment. It is characterized by the development of abnormal new retinal vessels. We describe a method for automatically detecting new vessels on the optic disc using retinal photography. Vessel-like candidate segments are first detected using a method based on watershed lines and ridge strength measurement. F...

增殖性糖尿病视网膜病变是一种罕见的疾病，可能导致严重的视力损伤。其特征是异常新生视网膜血管的生成。我们描述了一种使用视网膜摄影自动检测视盘上新血管的方法。首先，基于分水岭线和脊强度测量的方法检测血管样候选段。

Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.

从磁共振图像（MRI）中进行全自动全脑提取（也称为颅骨剥离）是大多数神经影像处理流程中的关键组成部分。作为流程中的第一步，其鲁棒性对整个系统的性能至关重要。尽管已有许多颅骨剥离方法被提出，但该问题尚未被认为得到完全解决。文献中的许多系统在特定数据集（通常是它们训练/调整所用的数据集）上表现良好，但当采集条件或研究对象群体不同时，却无法产生令人满意的结果。本文介绍了一种鲁棒的、基于学习的脑提取系统（ROBEX）。该方法结合了判别模型和生成模型以获得最终结果。判别模型是一个随机森林分类器，用于检测脑边界；生成模型是一个点分布模型，确保结果的合理性。当新图像输入系统时，生成模型被探索以找到根据判别模型具有最高可能性的轮廓。由于生成模型通常不能完美表示目标形状，因此使用图割对轮廓进行细化以获得最终分割。两个模型均使用来自专有数据集的92次扫描进行训练，但在各种其他数据集上实现了高度的鲁棒性。将ROBEX与六种其他流行的公开可用方法（BET、BSE、FreeSurfer、AFNI、BridgeBurner和GCUT）在三个公开数据集（IBSR、LPBA40和OASIS，共137次扫描）上进行了比较，这些数据集包含广泛的采集硬件和高度可变的人群（不同年龄组、健康/患病）。结果表明，对于几乎每个方法/数据集组合，ROBEX都提供了显著改进的性能指标。

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

可变形图像配准是医学图像处理中的一项基本任务。其最重要的应用包括：1) 多模态融合，其中不同成像设备或协议获取的信息被融合以促进诊断和治疗规划；2) 纵向研究，其中研究时间上的结构或解剖变化；以及3) 用于研究正常解剖变异的人群建模和统计图谱。本文试图对可变形配准方法进行概述，重点关注该领域的最新进展。此外，还特别强调了应用于医学图像的技术。为了深入研究图像配准方法，本文识别并独立研究了其主要组成部分。以系统的方式介绍了最新的技术。本文的贡献在于以系统的方式提供了配准技术的广泛描述。

Reconstruction of images in electrical impedance tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. The authors present a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse probl...

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Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods. >

中文摘要翻译待生成

We show an iterative reconstruction framework for diffraction ultrasound tomography. The use of broad-band illumination allows significant reduction of the number of projections compared to straight ray tomography. The proposed algorithm makes use of forward nonuniform fast Fourier transform (NUFFT) for iterative Fourier inversion. Incorporation of total variation regularization allows the reducti...

我们展示了一种用于衍射超声层析成像的迭代重建框架。与直射线层析成像相比，使用宽带照明可以显著减少投影数量。所提出的算法利用前向非均匀快速傅里叶变换（NUFFT）进行迭代傅里叶反演。结合总变差正则化可以减少...

A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.

开发了一套用于计算机分析表面发光显微镜（ELM）图像的系统，以增强恶性黑色素瘤的早期识别。作为初始步骤，通过几种基本分割算法和融合策略确定皮肤病变的二进制掩模。计算包含形状和辐射特征以及局部和全局参数的一组特征，以描述病变的恶性程度。然后通过应用统计特征子集选择方法从该组中选择显著特征。最终的kNN分类实现了87%的灵敏度和92%的特异性。

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods

我们提出了一种自动分割视网膜图像中血管网络的方法。该方法通过基于像素的特征向量将每个图像像素分类为血管或非血管，从而生成分割结果。特征向量由像素的强度和多尺度二维Gabor小波变换响应组成。Gabor小波能够调谐到特定频率，从而在单一步骤中实现噪声滤波和血管增强。我们使用贝叶斯分类器，其类条件概率密度函数（似然）描述为高斯混合，能够快速分类，同时能够建模复杂的决策表面。概率分布基于从手动分割获得的标记像素训练集进行估计。该方法在公开可用的DRIVE (Staal et al., 2004) 和 STARE (Hoover et al., 2000) 手动标记图像数据库上进行了性能评估。在DRIVE数据库上，它实现了0.9614的受试者工作特征曲线下面积，略优于现有最先进的方法。我们以开源MATLAB脚本的形式提供我们的实现，供有兴趣的实现细节、评估或方法开发的研究人员使用。

Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities $\sim 100\%$ of but at high FP levels. By leveraging exist...

自动计算机辅助检测（CADe）在临床实践和研究中已成为重要工具。最先进的方法通常以每名患者高假阳性（FP）率为代价实现高灵敏度。我们设计了一个两阶段的粗到细级联框架，首先运行一个候选生成系统，其灵敏度接近100%，但FP水平较高。通过利用现有...

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogra...

中文摘要翻译待生成

Reconstructing low-dose X-ray computed tomography (CT) images is a noise problem. This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations. Three different implementations were studied for the PWLS minimization. One utilizes a Markov random field (MRF) Gibbs functional to consider spatial correlations among nearby detector bins and projection views in sinogram space and minimizes the PWLS cost function by iterative Gauss-Seidel algorithm. Another employs Karhunen-Loeve (KL) transform to de-correlate data signals among nearby views and minimizes the PWLS adaptively to each KL component by analytical calculation, where the spatial correlation among nearby bins is modeled by the same Gibbs functional. The third one models the spatial correlations among image pixels in image domain also by a MRF Gibbs functional and minimizes the PWLS by iterative successive over-relaxation algorithm. In these three implementations, a quadratic functional regularization was chosen for the MRF model. Phantom experiments showed a comparable performance of these three PWLS-based methods in terms of suppressing noise-induced streak artifacts and preserving resolution in the reconstructed images. Computer simulations concurred with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS implementation may have the advantage in terms of computation for high-resolution dynamic low-dose CT imaging.

重建低剂量X射线计算机断层扫描（CT）图像是一个噪声问题。本研究探讨了一种惩罚加权最小二乘（PWLS）方法，以二维方式解决此问题，其中WLS考虑了一阶和二阶噪声矩，惩罚项则对信号空间相关性建模。研究了三种不同的PWLS最小化实现方法。一种是利用马尔可夫随机场（MRF）吉布斯泛函来考虑正弦图空间中相邻探测器箱和投影视角之间的空间相关性，并通过迭代高斯-赛德尔算法最小化PWLS代价函数。另一种采用卡洛南-洛伊（KL）变换来消除邻近视角数据信号的相关性，并通过解析计算自适应地最小化每个KL分量的PWLS，其中相邻箱之间的空间相关性由相同的吉布斯泛函建模。第三种是在图像域中同样通过MRF吉布斯泛函对图像像素间的空间相关性建模，并通过迭代逐次超松弛算法最小化PWLS。在这三种实现中，均选择了二次泛函正则化用于MRF模型。体模实验表明，这三种基于PWLS的方法在抑制噪声引起的条纹伪影和保持重建图像分辨率方面性能相当。计算机模拟在噪声-分辨率权衡和低对比度环境下的可检测性方面与体模实验结果一致。KL-PWLS实现在计算方面可能具有优势，适用于高分辨率动态低剂量CT成像。

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 $\,\times\,$ 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( $\sim 85.5\%$ ) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

自动组织表征是计算机辅助诊断（CAD）系统用于间质性肺疾病（ILD）的最关键组成部分之一。尽管该领域已经进行了大量研究，但问题仍然具有挑战性。深度学习技术最近在多种计算机视觉问题中取得了令人印象深刻的成果，这提高了人们对其可能应用于此领域的期望。

Algebraic reconstruction methods, such as the algebraic reconstruction technique (ART) and the related simultaneous ART (SART), reconstruct a two-dimensional (2-D) or three-dimensional (3-D) object from its X-ray projections. The algebraic methods have, in certain scenarios, many advantages over the more popular Filtered Backprojection approaches and have also recently been shown to perform well f...

代数重建方法，如代数重建技术(ART)及其相关的同步ART(SART)，通过X射线投影重建二维或三维物体。在某些情况下，代数方法比更流行的滤波反投影方法具有许多优点，并且最近也被证明在...中表现良好。

It is known that when a tissue is subjected to movements such as rotation, shearing, scaling, etc., changes in speckle patterns that result act as a noise source, often responsible for most of the displacement-estimate variance. From a modeling point of view, these changes can be thought of as resulting from two mechanisms: one is the motion of the speckles and the other, the alterations of their ...

已知当组织受到旋转、剪切、缩放等运动时，产生的散斑图案变化会作为噪声源，通常是位移估计方差的主要来源。从建模角度来看，这些变化可归因于两种机制：一是散斑的运动，二是其形状的改变。

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of whic...

自动检测淋巴结中的癌症转移灶有潜力改善患者的预后评估。为了公正比较用于此目的的算法，我们在墨尔本举办的IEEE国际生物医学成像研讨会2017会议上设立了CAMELYON17挑战赛。超过300名参与者在挑战赛网站上注册，其中

This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.

本文介绍了我们在设计用于结肠镜视频中息肉计算机辅助检测（CAD）系统的研究成果。我们的系统基于一种混合上下文-形状方法，利用上下文信息去除非息肉结构，并利用形状信息可靠地定位息肉。具体来说，给定一张结肠镜图像，我们首先获取粗略的边缘图。其次，通过我们独特的特征提取和边缘分类方案，从边缘图中去除非息肉边缘。第三，利用我们新颖的投票方案，在精细化的边缘图中以概率置信度分数定位息肉候选区域。所提出的CAD系统使用两个公开的息肉数据库进行了测试：CVC-ColonDB包含300张结肠镜图像，来自15个不同息肉的共300个息肉实例；以及ASU-Mayo数据库，这是我们收集的结肠镜视频，包含19,400帧，来自10个不同息肉的共5,200个息肉实例。我们使用自由响应受试者工作特征（FROC）分析评估了系统。在每帧0.1个假阳性时，系统在CVC-ColonDB上的灵敏度为88.0%，在ASU-Mayo数据库上的灵敏度为48%。此外，我们使用一种新的检测延迟分析来评估系统，延迟定义为从息肉首次出现在结肠镜视频中到系统首次检测到它的时间。在每帧0.05个假阳性时，系统的息肉检测延迟为0.3秒。

A direct (noniterative) reconstruction algorithm for electrical impedance tomography in the two-dimensional (2-D), cross-sectional geometry is reviewed. New results of a reconstruction of a numerically simulated phantom chest are presented. The algorithm is based on the mathematical uniqueness proof by A.I. Nachman [1996] for the 2-D inverse conductivity problem. In this geometry, several of the c...

回顾了一种用于二维（2-D）横截面几何的电阻抗断层成像的直接（非迭代）重建算法。给出了数值模拟的胸部体模重建的新结果。该算法基于A.I. Nachman [1996] 对二维逆电导率问题的数学唯一性证明。在这种几何结构中，几个...

A generalized expectation-maximization (GEM) algorithm is developed for Bayesian reconstruction, based on locally correlated Markov random-field priors in the form of Gibbs functions and on the Poisson data model. For the M-step of the algorithm, a form of coordinate gradient ascent is derived. The algorithm reduces to the EM maximum-likelihood algorithm as the Markov random-field prior tends towa...

中文摘要翻译待生成

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

受深度学习最新进展的启发，我们提出了一种框架，利用卷积神经网络（CNN）的深度级联，从未完全采样的数据中重建二维心脏磁共振（MR）图像的动态序列，以加速数据采集过程。具体来说，我们处理使用激进笛卡尔欠采样获取数据的情况。首先，我们展示了当每个二维图像帧独立重建时，所提方法在重建误差和重建速度方面优于最先进的二维压缩感知方法，如基于字典学习的MR图像重建。其次，当联合重建序列的帧时，我们证明了CNN可以通过组合卷积和数据共享方法有效地学习时空相关性。我们展示了所提方法始终优于最先进的方法，并且能够在高达11倍的欠采样下更忠实地保留解剖结构。此外，重建速度非常快：每个完整的动态序列可以在不到10秒内重建，对于二维情况，每个图像帧可以在23毫秒内重建，从而实现实时应用。

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotat...

缺乏公开可用的真实标注数据已被确定为将深度学习的最新发展应用于生物医学成像领域的主要挑战。尽管众包已实现对真实世界图像大规模数据库的标注，但其在生物医学领域的应用需要更深入的理解，从而对实际标注进行更精确的定义……