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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...

Image interpolation techniques often are required in medical imaging for image generation (e.g., discrete back projection for inverse Radon transform) and processing such as compression or resampling. Since the ideal interpolation function spatially is unlimited, several interpolation kernels of finite size have been introduced. This paper compares 1) truncated and windowed sine; 2) nearest neighbor; 3) linear; 4) quadratic; 5) cubic B-spline; 6) cubic; g) Lagrange; and 7) Gaussian interpolation and approximation techniques with kernel sizes from 1/spl times/1 up to 8/spl times/8. The comparison is done by: 1) spatial and Fourier analyses; 2) computational complexity as well as runtime evaluations; and 3) qualitative and quantitative interpolation error determinations for particular interpolation tasks which were taken from common situations in medical image processing. For local and Fourier analyses, a standardized notation is introduced and fundamental properties of interpolators are derived. Successful methods should be direct current (DC)-constant and interpolators rather than DC-inconstant or approximators. Each method's parameters are tuned with respect to those properties. This results in three novel kernels, which are introduced in this paper and proven to be within the best choices for medical image interpolation: the 6/spl times/6 Blackman-Harris windowed sinc interpolator, and the C2-continuous cubic kernels with N=6 and N=8 supporting points. For quantitative error evaluations, a set of 50 direct digital X-rays was used. They have been selected arbitrarily from clinical routine. In general, large kernel sizes were found to be superior to small interpolation masks. Except for truncated sine interpolators, all kernels with N=6 or larger sizes perform significantly better than N=2 or N=3 point methods (p/spl Lt/0.005). However, the differences within the group of large-sized kernels were not significant. Summarizing the results, the cubic 6/spl times/6 interpolator with continuous second derivatives, as defined in (24), can be recommended for most common interpolation tasks. It appears to be the fastest six-point kernel to implement computationally. It provides eminent local and Fourier properties, is easy to implement, and has only small errors. The same characteristics apply to B-spline interpolation, but the 6/spl times/6 cubic avoids the intrinsic border effects produced by the B-spline technique. However, the goal of this study was not to determine an overall best method, but to present a comprehensive catalogue of methods in a uniform terminology, to define general properties and requirements of local techniques, and to enable the reader to select that method which is optimal for his specific application in medical imaging.

图像插值技术在医学成像中常用于图像生成（例如，用于逆拉东变换的离散反投影）以及处理如压缩或重采样。由于理想插值函数在空间上是无限的，因此引入了多种有限大小的插值核。本文比较了：1）截断和加窗sinc；2）最近邻；3）线性；4）二次；5）三次B样条；6）三次；7）拉格朗日；以及8）高斯插值和近似技术，核大小从1×1到8×8。比较通过以下方式进行：1）空间和傅里叶分析；2）计算复杂度及运行时间评估；以及3）针对医学图像处理中常见情况的特定插值任务的定性和定量插值误差确定。对于局部和傅里叶分析，引入了标准化符号并推导了插值器的基本属性。成功的方法应为直流（DC）恒定且为插值器，而非DC不恒定或近似器。每种方法的参数根据这些属性进行调整。这产生了三个新颖的核，在本文中介绍并证明是医学图像插值的最佳选择之一：6×6 Blackman-Harris窗sinc插值器，以及具有N=6和N=8支撑点的C2连续三次核。对于定量误差评估，使用了50张直接数字X光片。这些X光片是从临床常规中任意选取的。总的来说，大核尺寸优于小插值掩模。除截断sinc插值器外，所有N=6或更大尺寸的核性能显著优于N=2或N=3点方法（p<<0.005）。然而，大尺寸核组内的差异并不显著。总结结果，如（24）中定义的具有连续二阶导数的三次6×6插值器可推荐用于大多数常见插值任务。它似乎是计算上最快的六点核。它提供了卓越的局部和傅里叶特性，易于实现，且误差很小。相同的特性也适用于B样条插值，但6×6三次插值避免了B样条技术产生的固有边界效应。然而，本研究的目标并非确定总体最佳方法，而是以统一的术语提供方法的全面目录，定义局部技术的一般属性和要求，并使读者能够选择最适合其特定医学成像应用的方法。

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative advers...

欠采样磁共振图像重建通常是一个不适定的线性逆问题。时间和资源密集型的计算需要在准确性和速度之间进行权衡。此外，最先进的压缩感知分析并不考虑图像的诊断质量。为了解决这些挑战，我们提出了一种新颖的压缩感知框架，该框架使用生成对抗...

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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Magnetic detection of complex images in magnetic resonance imaging (MRI) is immune to the effects of incidental phase variations, although in some applications information is lost or images are degraded. It is suggested that synchronous detection or demodulation can be used in MRI systems in place of magnitude detection to provide complete suppression of undesired quadrature components, to preserv...

中文摘要翻译待生成

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...

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。

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。

Describes an extrinsic-point-based, interactive image-guided neurosurgical system designed at Vanderbilt University, Nashville, TN, as part of a collaborative effort among the Departments of Neurological Surgery, Computer Science, and Biomedical Engineering. Multimodal image-to-image (II) and image-to-physical (IP) registration is accomplished using implantable markers. Physical space tracking is accomplished with optical triangulation. The authors investigate the theoretical accuracy of point-based registration using numerical simulations, the experimental accuracy of their system using data obtained with a phantom, and the clinical accuracy of their system using data acquired in a prospective clinical trial by 6 neurosurgeons at 4 medical centers from 158 patients undergoing craniotomies to respect cerebral lesions. The authors can determine the position of their markers with an error of approximately 0.4 mm in X-ray computed tomography (CT) and magnetic resonance (MR) images and 0.3 mm in physical space. The theoretical registration error using 4 such markers distributed around the head in a configuration that is clinically practical is approximately 0.5-0.6 mm. The mean CT-physical registration error for the: phantom experiments is 0.5 mm and for the clinical data obtained with rigid head fixation during scanning is 0.7 mm. The mean CT-MR registration error for the clinical data obtained without rigid head fixation during scanning is 1.4 mm, which is the highest mean error that the authors observed. These theoretical and experimental findings indicate that this system is an accurate navigational aid that can provide real-time feedback to the surgeon about anatomical structures encountered in the surgical field.

描述了一种基于外部点的交互式图像引导神经外科系统，由田纳西州纳什维尔的范德比尔特大学设计，是神经外科、计算机科学和生物医学工程系合作的一部分。使用植入式标记实现多模态图像到图像（II）和图像到物理（IP）配准。物理空间跟踪通过光学三角测量完成。作者通过数值模拟研究了基于点的配准的理论精度，使用体模数据验证系统的实验精度，并通过6位神经外科医生在4个医疗中心对158名接受开颅手术以切除脑部病变的患者的前瞻性临床试验数据评估了系统的临床精度。作者能够以约0.4毫米的误差在X射线计算机断层扫描（CT）和磁共振（MR）图像中确定标记位置，在物理空间中误差为0.3毫米。使用4个分布头部周围且临床实用的标记的理论配准误差约为0.5-0.6毫米。体模实验中CT-物理配准的平均误差为0.5毫米，扫描时使用刚性头部固定的临床数据平均误差为0.7毫米。扫描时未使用刚性头部固定的临床数据中CT-MR配准的平均误差为1.4毫米，这是作者观察到的最高平均误差。这些理论和实验结果表明，该系统是一种精确的导航辅助工具，可为外科医生提供手术区域中遇到的解剖结构的实时反馈。

In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the o...

在金属植入物存在的情况下，X射线计算机断层扫描（CT）图像中会出现金属伪影。尽管过去几十年提出了大量的金属伪影减少（MAR）方法，但MAR仍然是临床X射线CT的主要问题之一。本文开发了一种基于卷积神经网络（CNN）的开放式MAR框架，该框架融合了来自...的信息

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。

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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Ultrafast ultrasonic imaging is a rapidly developing field based on the unfocused transmission of plane or diverging ultrasound waves. This recent approach to ultrasound imaging leads to a large increase in raw ultrasound data available per acquisition. Bigger synchronous ultrasound imaging datasets can be exploited in order to strongly improve the discrimination between tissue and blood motion in the field of Doppler imaging. Here we propose a spatiotemporal singular value decomposition clutter rejection of ultrasonic data acquired at ultrafast frame rate. The singular value decomposition (SVD) takes benefits of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters based on high pass temporal filtering. Whereas classical clutter filters operate on the temporal dimension only, SVD clutter filtering provides up to a four-dimensional approach (3D in space and 1D in time). We demonstrate the performance of SVD clutter filtering with a flow phantom study that showed an increased performance compared to other classical filters (better contrast to noise ratio with tissue motion between 1 and 10mm/s and axial blood flow as low as 2.6 mm/s). SVD clutter filtering revealed previously undetected blood flows such as microvascular networks or blood flows corrupted by significant tissue or probe motion artifacts. We report in vivo applications including small animal fUltrasound brain imaging (blood flow detection limit of 0.5 mm/s) and several clinical imaging cases, such as neonate brain imaging, liver or kidney Doppler imaging.

超快超声成像是一个快速发展的领域，基于平面或发散超声波的非聚焦发射。这种最新的超声成像方法使得每次采集可获得的原始超声数据大幅增加。可以利用更大的同步超声成像数据集来强有力地改善多普勒成像中组织和血液运动的区分。本文提出了一种对超快帧率采集的超声数据进行时空奇异值分解杂波抑制的方法。奇异值分解（SVD）利用了组织和血液运动在时空相干性方面的不同特征，并且比基于高通时间滤波的传统杂波抑制滤波器性能更强。经典杂波滤波器仅在时间维度上操作，而SVD杂波滤波提供了高达四维（三维空间和一维时间）的方法。我们通过流动体模研究展示了SVD杂波滤波的性能，与其他经典滤波器相比，其性能有所提高（在组织运动1-10毫米/秒和轴向血流低至2.6毫米/秒的情况下具有更好的对比度噪声比）。SVD杂波滤波揭示了先前未检测到的血流，例如微血管网络或受到显著组织或探头运动伪影影响的血流。我们报告了体内应用，包括小动物功能性超声脑成像（血流检测极限为0.5毫米/秒）以及几个临床成像案例，如新生儿脑成像、肝脏或肾脏多普勒成像。

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix. The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties

统计迭代重建方法的图像质量取决于通过系统矩阵对图像空间和投影空间之间关系的精确建模。临床Hi-Rez扫描仪的系统矩阵元素是通过处理视场部分内不同位置的点源测量数据得到的。这些测量数据包括轴向压缩和相邻投影的方位角交错。测量数据针对晶体效率和几何效率进行了校正。然后，通过处理投影空间中的响应推导出整个系统矩阵。这些响应包括系统矩阵的几何和检测物理分量。对响应进行了参数化，以校正点源位置并平滑投影噪声。该模型还考虑了扫描仪使用的轴向压缩（跨度）。利用估计的响应参数构建了迭代重建的前向投影器。本文将我们之前的工作扩展到全三维。使用实验数据比较了标准迭代重建软件和模拟响应函数的重建图像。结果表明，响应函数的建模改善了空间分辨率和噪声特性。

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 emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

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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。

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……

The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classifi...

作者提出了一种基于模型的方法，用于全自动校正MR脑图像的偏置场。MR信号被建模为一个随机过程的实现，该过程具有参数化概率分布，并受到平滑多项式非均匀性或偏置场的干扰。作者提出的方法应用了迭代期望最大化（EM）策略，该策略交错进行像素分类...

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...

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。

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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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...

Object tracking is a key enabling technology in the context of computer-assisted medical interventions. Allowing the continuous localization of medical instruments and patient anatomy, it is a prerequisite for providing instrument guidance to subsurface anatomical structures. The only widely used technique that enables real-time tracking of small objects without line-of-sight restrictions is electromagnetic (EM) tracking. While EM tracking has been the subject of many research efforts, clinical applications have been slow to emerge. The aim of this review paper is therefore to provide insight into the future potential and limitations of EM tracking for medical use. We describe the basic working principles of EM tracking systems, list the main sources of error, and summarize the published studies on tracking accuracy, precision and robustness along with the corresponding validation protocols proposed. State-of-the-art approaches to error compensation are also reviewed in depth. Finally, an overview of the clinical applications addressed with EM tracking is given. Throughout the paper, we report not only on scientific progress, but also provide a review on commercial systems. Given the continuous debate on the applicability of EM tracking in medicine, this paper provides a timely overview of the state-of-the-art in the field.

对象追踪是计算机辅助医学干预中的一项关键使能技术。通过连续定位医疗器械和患者解剖结构，它为深层解剖结构提供器械引导的前提条件。唯一广泛使用的、无需视线限制即可实时追踪小物体的技术是电磁（EM）追踪。尽管电磁追踪已成为许多研究的主题，但临床应用进展缓慢。因此，本综述旨在深入了解电磁追踪在医学中的未来潜力和局限性。我们描述了电磁追踪系统的基本工作原理，列出了主要误差来源，并总结了已发表的关于追踪精度、准确性和鲁棒性的研究以及相应的验证协议。此外，还深入综述了最新的误差补偿方法。最后，概述了电磁追踪所涉及的临床应用。全文不仅报告了科学进展，还对商业系统进行了综述。鉴于关于电磁追踪在医学中适用性的持续争论，本文及时概述了该领域的最新进展。