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This paper describes fusion of three-dimensional (3-D) ultrasound (US) and magnetic resonance imaging (MRI) data sets, without the assistance of external fiducial markers or external position sensors. Fusion of these two modalities combines real-time 3-D ultrasound scans of soft tissue with the larger anatomical framework from MRI. The complementary information available from multiple imaging moda...

本文描述了在不使用外部基准标记或外部位置传感器的情况下，三维超声与磁共振成像数据集的融合。这两种模态的融合将软组织的实时三维超声扫描与MRI提供的更大解剖框架相结合。多模态成像提供的互补信息...

Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q -sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q -ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.

基于扩散磁共振（MR）信号与潜在扩散位移之间的傅里叶变换关系，推导出一个新的关系式，可直接从扩散MR信号估计自旋分布函数（SDF）。这一关系引出了一项称为广义q-采样成像（GQI）的成像方法，该方法可以从q-球成像（QBI）中使用的壳采样方案或扩散频谱成像（DSI）中使用的网格采样方案获得SDF。通过仿真研究和与QBI及DSI的体内实验比较，评估了GQI的准确性。仿真结果表明，GQI的准确性与QBI和DSI相当。GQI的仿真研究还显示，一个名为定量各向异性的各向异性指数与所解析纤维成分的体积分数相关。GQI的体内图像显示，SDF模式与QBI或DSI重建的ODF相似。从GQI生成的纤维追踪也与从QBI和DSI生成的相似。总之，所提出的GQI方法可应用于网格或壳采样方案，并能提供关于交叉纤维的方向和定量信息。

The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-t...

本文讨论了从莱斯分布数据（例如，磁共振图像的幅度）进行参数估计的问题。讨论了传统估计方法的性质，并与已知渐近最优的最大似然（ML）估计进行了比较。与先前提出的方法相反，ML估计被证明在高信噪比条件下是无偏的...

Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical-image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation. In this paper, the authors utilize a recently proposed oscillator network called the locally excitatory globall...

可视化技术和专业图形工作站的进步使得临床医生能够与采样医学图像数据集中的解剖结构进行虚拟交互。有效利用这一技术的障碍是图像分割这一难题。在本文中，作者利用了一种最近提出的振荡器网络，称为局部兴奋全局...

A novel method of reconstruction from single-photon emission computerized tomography data is proposed. This method builds on the expectation-maximization (EM) approach to maximum likelihood reconstruction from emission tomography data, but aims instead at maximum posterior probability estimation, which takes account of prior belief about smoothness in the isotope concentration. A novel modificatio...

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Presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail. The initial low-dimensional registration i...

介绍了猕猴枕叶和全脑冷冻切片图像以及通过磁共振成像的人类大脑深层脑结构的三维（3-D）解剖图像数据的微分同胚变换。这些变换以层次方式生成，兼顾全局和局部解剖细节。初始的低维配准...

Presents a validation study of an optical-flow method for the rapid estimation of myocardial displacement in magnetic resonance tagged cardiac images. This registration and change visualization (RCV) software uses a hierarchical estimation technique to compute the flow field that describes the warping of an image of one cardiac phase into alignment with the next. This method overcomes the requirem...

介绍了一种用于快速估计磁共振标记心脏图像中心肌位移的光流方法的验证研究。该配准与变化可视化（RCV）软件使用分层估计技术来计算描述一个心脏相位图像变形以与下一个相位对齐的光流场。该方法克服了需要...

Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image qu...

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An approach for tracking and quantifying the nonrigid, nonuniform motion of the left ventricular (LV) endocardial wall from two-dimensional (2-D) cardiac image sequences, on a point-by-point basis over the entire cardiac cycle, is presented. Given a set of boundaries, motion computation involves first matching local segments on one contour to segments on the next contour in the sequence using a sh...

提出了一种在整个心动周期中逐点追踪和量化左心室心内膜壁非刚性、非均匀运动的方法，该方法基于二维心脏图像序列。给定一组边界，运动计算首先涉及使用形状匹配将一个轮廓上的局部段与序列中下一个轮廓上的段进行匹配。

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation ...

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We present for the first time a full three-dimensional (3-D) reconstruction of absorption images of breast from continuous-wave (cw) measurements performed on a premenopausal woman. Our 3-D optical images clearly reveal a large primary tumor as well as a small secondary tumor in a separate location of the breast. The multiple tumors identified by our 3-D optical imaging have been confirmed by the ...

我们首次展示了在一位绝经前女性身上进行的连续波（CW）测量所得到的乳腺吸收图像的全三维（3D）重建。我们的3D光学图像清晰地揭示了乳腺内一个大的原发肿瘤以及另一个位置的一个小的继发肿瘤。我们的3D光学成像识别出的多个肿瘤已通过...得到证实。

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

皮肤镜图像中的黑色素瘤自动识别是一项极具挑战性的任务，原因在于皮肤病变对比度低、黑色素瘤类内差异大、黑色素瘤与非黑色素瘤病变视觉相似度高以及图像中存在大量伪影。为应对这些挑战，我们提出了一种利用非常深卷积神经网络（CNN）进行黑色素瘤识别的新方法。与现有使用低级手工特征或浅层CNN的方法相比，我们的深层网络（超过50层）能够获取更丰富、更具判别性的特征，从而实现更准确的识别。为充分利用非常深网络，我们提出了一系列方案来确保在有限训练数据下进行有效训练和学习。首先，我们应用残差学习来处理网络加深时的退化和过拟合问题。该技术可确保我们的网络因深度增加而受益于性能提升。然后，我们构建了一个全卷积残差网络（FCRN）用于精确的皮肤病变分割，并通过集成多尺度上下文信息方案进一步增强其能力。最后，我们将提出的FCRN（用于分割）与其他非常深残差网络（用于分类）无缝集成，形成两阶段框架。该框架使分类网络能够基于分割结果而非整个皮肤镜图像提取更具代表性和特异性的特征，进一步缓解了训练数据不足的问题。该框架在ISBI 2016皮肤病变分析向黑色素瘤检测挑战数据集上进行了广泛评估。实验结果表明，所提框架性能显著提升，在分类和分割任务中分别位列25个团队和28个团队中的第一和第二。本研究证实，即使训练数据有限，具有有效训练机制的非常深CNN也能用于解决复杂的医学图像分析任务。

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering ...

介绍了一个自动分割和标记人脑磁共振图像（MRI）中胶质母细胞瘤的系统。MRI由T1加权、质子密度和T2加权特征图像组成，通过集成基于知识（KB）技术与多光谱分析的系统进行处理。初始分割通过无监督聚类进行...

We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism...

我们提出了一种新的图像重建方法，该方法将投影梯度下降（PGD）中的投影器替换为卷积神经网络（CNN）。最近，训练为图像到图像回归器的CNN已成功用于解决成像中的逆问题。然而，与现有的迭代图像重建算法不同，这些基于CNN的方法通常缺乏反馈机制...

Proposes and validates the hypothesis that one can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point correspondence maps. The noninvasive, algorithm-derived results are validated on two levels...

提出并验证了一种假设，即可以利用心肌表面的微分形状特性从标准三维（3-D）图像序列（MRI和CT）中恢复密集场运动。进一步可以从点对应图中推断出左心室区域功能的定量测量。无创、算法推导的结果在两个层面上得到验证...

The authors describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for (1) global tissue class volumes; (2) standardized linear model [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and (3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. The authors describe novel methods for economically ascertaining probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation testing is generally excellent; whereas theoretical distributions may be over conservative. Permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.

作者描述了几乎完全自动化的程序，用于估计全局、体素和簇级统计量，以检验两组结构磁共振成像（MRI）数据之间神经解剖学差异为零的零假设。零假设下的理论分布可用于（1）全局组织类别体积；（2）标准化线性模型[分析...

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case ( i.e. , viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.

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This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the “MICCAI 2007 Grand Challenge” workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

本文对10种自动方法和6种交互式方法在对比增强CT图像中的肝脏分割进行了比较研究。该研究基于“MICCAI 2007大挑战”研讨会的结果，其中16个团队在共同数据库上评估了他们的算法。提供了20幅带有参考分割的临床图像，用于预先训练和调整算法。参与者也被允许为此目的使用额外的专有训练数据。然后，所有团队必须将他们的方法应用于10个测试数据集，并提交获得的结果。所使用的算法包括统计形状模型、图谱配准、水平集、图割和基于规则的系统。所有结果与参考分割通过五种误差度量进行比较，这些度量突出了分割精度的不同方面。所有度量根据一个特定的评分系统进行组合，该评分系统将获得的值与人类专家的变异性相关联。总体而言，交互式方法达到了比自动方法更高的平均分数，并具有更好的分割质量一致性。然而，最好的自动方法（主要基于统计形状模型和一些额外的自由变形）在大多数测试图像上能够很好地竞争。该研究提供了对不同分割方法在现实条件下性能的深入了解，并突出了当前图像分析技术的成就和局限性。

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni , 2005 and Makni , 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activat...

fMRI的个体内分析主要解决两个问题：检测诱发活动的脑区域以及估计潜在的动态过程。在Makni等人2005年和Makni等人2008年的研究中，提出了一种检测-估计框架来联合处理这些问题，因为它们相互关联。在贝叶斯形式中，通过建模激活来实现检测……

In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values, from observed image data. Many of ...

近年来，许多研究者提出使用吉布斯先验模型来正则化从发射计算机断层扫描数据重建的图像。不幸的是，用于指定吉布斯先验的超参数会极大影响这些先验所施加的正则化程度，因此，已提出许多从观测图像数据估计超参数值的方法。许多...

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.

自动视网膜图像分析正成为早期检测眼病的重要筛查工具。青光眼是导致失明的最常见原因之一。视盘的手动检查是用于检测青光眼的标准程序。本文提出了一种基于从单目视网膜图像中分割出的视盘和视杯区域的自动视盘参数化技术。提出了一种新颖的视盘分割方法，该方法在多维特征空间中整合每个兴趣点周围的局部图像信息，以增强对视盘区域及其周围变化的鲁棒性。我们还提出了一种新颖的视杯分割方法，该方法基于解剖学证据，如视杯边界处的血管弯曲，这些弯曲被青光眼专家认为是相关的。使用支持区域概念稳健地检测血管弯曲，该概念自动选择正确的分析尺度。采用多阶段策略来获取称为r-bends的可靠血管弯曲子集，然后进行局部样条拟合以得出所需的视杯边界。该方法在138张图像（包括33张正常和105张青光眼图像）上进行了评估，并与三位青光眼专家进行了比较。获得的分割结果显示，该方法在处理数据集中发现的各种几何和光度变化方面具有一致性。该方法对垂直杯盘直径比的估计误差为0.09/0.08（均值/标准差），对杯盘面积比的估计误差为0.12/0.10。总体而言，获得的定性和定量结果表明，该方法在分割和后续的青光眼评估中的视盘参数化方面均有效。

Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.

手术工作流程识别具有众多潜在的医学应用，例如手术视频数据库的自动索引和实时手术室调度的优化等。因此，在多种手术（如白内障手术、神经外科手术和腹腔镜手术）的背景下，手术阶段识别已被研究。文献中通常使用两种特征来执行此任务：视觉特征和工具使用信号。然而，所使用的视觉特征大多是手工设计的。此外，工具使用信号通常通过手动注释过程或使用额外设备来收集。在本文中，我们提出了一种新的阶段识别方法，该方法使用卷积神经网络（CNN）从胆囊切除术视频中自动学习特征，并且仅依赖视觉信息。以往的研究表明，工具使用信号可以为执行阶段识别任务提供有价值的信息。因此，我们提出了一种新的CNN架构，称为EndoNet，旨在以多任务方式执行阶段识别和工具存在检测任务。据我们所知，这是首次提出使用CNN进行腹腔镜视频多识别任务的工作。与其他方法的实验比较表明，EndoNet在两项任务上均达到了最先进的结果。

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate ...

临床注释，如体素级二元或概率组织分割、结构分区、病理感兴趣区域和解剖标志，是许多临床研究的关键。然而，由于手动生成这些注释耗时，它们往往稀缺且仅限于小数据子集。本文探索了一种新颖的框架来传播...

Images formed with endoscopes suffer from a spatial distortion due to the wide-angle nature of the endoscope's objective lens. This change in the size of objects with position precludes quantitative measurement of the area of the objects, which is important in endoscopy for accurately measuring ulcer and lesion sizes over time. A method for correcting the distortion characteristic of endoscope ima...

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This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

本文综述了超声分割方法，广义上侧重于医学B型超声图像技术。首先，我们通过临床应用回顾文章，突出已研究的方法和不同临床领域中的验证程度。然后，我们根据先验信息的使用对方法进行分类。最后，我们选出十篇提出了原创思想的论文，这些思想在超声分割问题上显示出特别的临床实用性或潜力。