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Transformer-based methods are recently popular in vision tasks because of their capability to model global dependencies alone. However, it limits the performance of networks due to the lack of modeling local context and global-local correlations of multi-scale features. In this paper, we present MISSFormer, a Medical Image Segmentation tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two appealing designs: 1) a feed-forward network in transformer block of U-shaped encoder-decoder structure is redesigned, ReMix-FFN, which explore global dependencies and local context for better feature discrimination by re-integrating the local context and global dependencies; 2) a ReMixed Transformer Context Bridge is proposed to extract the correlations of global dependencies and local context in multi-scale features generated by our hierarchical transformer encoder. The MISSFormer shows a solid capacity to capture more discriminative dependencies and context in medical image segmentation. The experiments on multi-organ, cardiac segmentation and retinal vessel segmentation tasks demonstrate the superiority, effectiveness and robustness of our MISSFormer. Specifically, the experimental results of MISSFormer trained from scratch even outperform state-of-the-art methods pre-trained on ImageNet, and the core designs can be generalized to other visual segmentation tasks. The code has been released on Github: https://github.com/ZhifangDeng/MISSFormer .

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Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning. ResViT’s generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics.

中文摘要翻译待生成

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。

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 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...

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

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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。

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

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……

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。

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。

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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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

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。

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。

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...

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 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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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...

We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

我们提出了一种用于心脏计算机断层扫描（CT）容积定量功能分析的自动四腔心分割系统。讨论了两个主题：心脏建模和对未知容积的自动模型拟合。心脏建模是一项非平凡的任务，因为心脏是一个复杂的非刚性器官。模型必须解剖学准确，允许手动编辑，并提供足够的信息来指导自动检测和分割。与以往的工作不同，我们在模型的控制点中显式地表示了重要的标志点（如瓣膜和室间隔尖）。这些控制点可以可靠地检测到，以指导自动模型拟合过程。利用该模型，我们开发了一种高效且鲁棒的方法，用于三维CT容积中的自动心脏腔室分割。我们将分割公式化为一个两步学习问题：解剖结构定位和边界描绘。在这两个步骤中，我们利用了判别模型学习的最新进展。引入了一种新颖的算法——边缘空间学习（MSL）来解决用于定位心腔的9维相似变换搜索问题。在确定心腔的姿态后，我们通过基于学习的边界描绘来估计三维形状。该方法已在文献报道的最大数据集（来自137名患者的323个容积）上进行了广泛测试。据我们所知，我们的系统是最快的，所有四个腔室的自动分割速度为每个容积4.0秒（在双核3.2-GHz处理器上）。

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.

血流动力学因素被认为与颅内动脉瘤的进展和破裂有关。当前的研究旨在探究血流动力学特征（如动脉瘤内血流模式的复杂性和稳定性、血流冲击区域的大小和位置）与动脉瘤破裂临床史之间的可能关联。然而，目前尚无可靠的方法在体内测量血流模式。本文介绍了一种基于医学图像对脑动脉瘤血流动力学进行患者特异性建模和表征的高效方法。同时，还呈现了在预期生理条件下多个变量变化对血流动力学特征影响的敏感性分析。该敏感性分析表明，尽管可以观察到速度场的变化，但当平均输入流量、流量分配、黏度模型或网格分辨率改变时，动脉瘤内血流模式的表征并未改变。研究还发现，对计算流场影响最大的变量是血管结构的几何形状。我们得出结论，利用所提出的建模流程，涉及大量脑动脉瘤的临床研究是可行的。

Infrared thermal imaging of the skin has been used for several decades to monitor the temperature distribution of human skin. Abnormalities such as malignancies, inflammation, and infection cause localized increases in temperature which show as hot spots or as asymmetrical patterns in an infrared thermogram. Even though it is nonspecific, infrared thermology is a powerful detector of problems that affect a patient's physiology. While the use of infrared imaging is increasing in many industrial and security applications, it has declined in medicine probably because of the continued reliance on first generation cameras. The transfer of military technology for medical use has prompted this reappraisal of infrared thermology in medicine. Digital infrared cameras have much improved spatial and thermal resolutions, and libraries of image processing routines are available to analyze images captured both statically and dynamically. If thermographs are captured under controlled conditions, they may be interpreted readily to diagnose certain conditions and to monitor the reaction of a patient's physiology to thermal and other stresses. Some of the major areas where infrared thermography is being used successfully are neurology, vascular disorders, rheumatic diseases, tissue viability, oncology (especially breast cancer), dermatological disorders, neonatal, ophthalmology, and surgery.

红外热成像已被用于监测人体皮肤温度分布数十年。恶性肿瘤、炎症和感染等异常情况会导致局部温度升高，在红外热像图中表现为热点或不对称模式。尽管非特异性，红外热学是检测影响患者生理问题的强大工具。虽然红外成像在许多工业和安全应用中的使用正在增加，但在医学中却有所下降，这可能是因为持续依赖第一代相机。军事技术向医疗用途的转移促使了这次对医学红外热学的重新评估。数字红外相机的空间和热分辨率大大提高，并且有图像处理程序库可用于分析静态和动态捕获的图像。如果在受控条件下拍摄热像图，可以很容易地解释它们以诊断某些疾病，并监测患者对外界冷热等刺激的生理反应。红外热成像成功应用的一些主要领域是神经病学、血管疾病、风湿病、组织活力、肿瘤学（尤其是乳腺癌）、皮肤病、新生儿、眼科和外科手术。

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。

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