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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...

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。

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。

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。

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...

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 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

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...

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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……

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of $5.69\pm 2.41\ \mu{\rm m}$ was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of $5.71\pm 1.98\ \mu{\rm m}$ .

随着谱域光学相干断层扫描（OCT）的引入，与上一代时域OCT相比，常规采集的图像数据集要大得多。因此，对用于处理此类数据的三维分割方法的需求变得越来越重要。我们报告了一种图论分割方法，用于同时分割多个三维表面，该方法在成本函数方面保证最优，并且直接适用于三维谱域OCT图像数据的分割。我们提出了对一般分层图分割方法的两个扩展：能够结合变化的可行性约束和能够结合真实的区域信息。从13名受试者的13幅谱域OCT图像的训练集中学习了适当的可行性约束和成本函数。训练后，我们的方法在来自14名受试者的28幅图像的测试集上进行了测试。在分割七个表面（六层）时，以两位眼科医生手动描记的平均值作为参考标准，实现了$5.69±2.41\ \mu{\rm m}$的总体平均无符号边界定位误差。这一结果与测得的$5.71±1.98\ \mu{\rm m}$观察者间变异性非常接近。

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.

青光眼是一种导致视力丧失的慢性眼病。由于无法治愈，及时检测该疾病至关重要。目前使用眼压（IOP）的测试对于基于人群的青光眼筛查不够敏感。视网膜眼底图像中的视神经头评估更有前景且更优越。本文提出使用超像素分类进行视盘和视杯分割，用于青光眼筛查。在视盘分割中，使用直方图和中心环绕统计将每个超像素分类为盘或非盘。计算自我评估可靠性分数以评估自动视盘分割的质量。对于视杯分割，除了直方图和中心环绕统计外，还将位置信息纳入特征空间以提高性能。所提出的分割方法在650张图像的数据集上进行了评估，这些图像的视盘和视杯边界由训练有素的专业人员手动标记。实验结果显示，视盘和视杯分割的平均重叠误差分别为9.5%和24.1%。结果还显示，随着可靠性分数的降低，重叠误差增加，这证明了自我评估的有效性。分割后的视盘和视杯随后用于计算杯盘比，以进行青光眼筛查。我们提出的方法在两个数据集中实现了0.800和0.822的曲线下面积，高于其他方法。这些方法可用于分割和青光眼筛查。自我评估将作为大误差病例的指标，并增强自动分割和筛查的临床部署。

Presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.

提出了一种新的图像配准方法，该方法基于联合估计两幅图像之间的正向和反向变换，同时限制这些变换互为逆变换。这种方法产生了一致性的变换集，其配准误差小于传统方法独立估计正向和反向变换的误差，即具有更好的对应性。变换通过迭代估计，并通过约束其遵循连续介质力学定律来限制拓扑保持。变换通过傅里叶级数参数化，以对角化连续介质力学约束施加的协方差结构，并提供计算高效的数值实现。使用线性弹性材料约束的结果在磁共振和X射线计算机断层扫描图像数据上展示。结果表明，在线性弹性约束下联合估计一致性的正向和反向变换，比单独使用任一约束或完全不使用约束给出更好的配准结果。

Presents an image reconstruction method for positron-emission tomography (PET) based on a penalized, weighted least-squares (PWLS) objective. For PET measurements that are precorrected for accidental coincidences, the author argues statistically that a least-squares objective function is as appropriate, if not more so, than the popular Poisson likelihood objective. The author proposes a simple dat...

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Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.

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Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet , a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction , which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available.

最近，密集连接在计算机视觉中引起了广泛关注，因为它们促进了训练期间的梯度流动和隐式深度监督。特别是，DenseNet以前馈方式将每一层连接到其他所有层，并在自然图像分类任务中表现出令人印象深刻的性能。我们提出了HyperDenseNet，一种3D全卷积神经网络，将密集连接的定义扩展到多模态分割问题。每个成像模态都有一个路径，密集连接不仅发生在同一路径内的层对之间，还发生在不同路径的层对之间。这与现有的多模态CNN方法形成对比，这些方法完全依赖单个联合层（或抽象级别）来融合多个模态，通常是在网络的输入或输出处。因此，提出的网络有完全的自由度来学习模态之间更复杂的组合，在所有抽象级别内部和之间，这显著增加了学习表示。我们在两个不同且高度竞争的多模态脑组织分割挑战中报告了广泛的评估，即iSEG 2017和MRBrainS 2013，前者关注六个月婴儿数据，后者关注成人图像。HyperDenseNet在许多最先进的分割网络上取得了显著改进，在两个基准测试中均排名第一。我们还提供了特征重用的全面实验分析，证实了超密集连接在多模态表示学习中的重要性。我们的代码已公开可用。

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the...

本文提出了一种新的用于视网膜图像血管分割的监督方法。该方法将分割任务重新塑造为从视网膜图像到血管图的跨模态数据转换问题。提出了一种具有强归纳能力的宽深度神经网络来建模这种转换，并提出了一种高效的训练策略。

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...

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adapti...

在广泛的语义分割任务中，全卷积神经网络已成功用于实现最先进的性能。全卷积网络的架构创新主要集中在改进空间编码或网络连接以辅助梯度流。在本文中，我们旨在向另一个方向，即自适应地重新校准学习到的特征图...

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...

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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

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.

中文摘要翻译待生成

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。

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...

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。