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Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address the...

卷积神经网络（CNN）在自动医学图像分割方面取得了最先进的性能。然而，它们尚未在临床应用中展现出足够准确和稳健的结果。此外，它们还受限于缺乏图像特定的适应性以及对先前未见过的对象类别（即零样本学习）缺乏泛化能力。为了解决这些问题...

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

自动化核检测是许多计算机辅助病理相关图像分析算法（如乳腺癌组织标本的自动分级）的关键步骤。诺丁汉组织学评分系统与组织病理学图像中乳腺癌细胞核的形状和外观高度相关。然而，自动核检测面临两个挑战：1) 大量细胞核和高分辨率数字化病理图像的尺寸；2) 单个细胞核在大小、形状、外观和纹理上的变异性。近年来，“深度学习”策略在大图像数据的分类和分析中引起了关注。组织病理学由于其规模和复杂性，是应用深度学习策略的绝佳案例。本文提出了一种堆叠稀疏自动编码器（SSAE），作为一种深度学习策略的实例，用于在乳腺癌的高分辨率组织病理学图像上进行高效的细胞核检测。SSAE仅从像素强度中学习高层特征，以识别细胞核的区分性特征。对每幅图像应用滑动窗口操作，通过自编码器获取的高层特征表示图像块，然后将其输入分类器，将每个图像块分类为核或非核。在500张组织病理学图像（2200×2200）和约3500个手动分割的单个细胞核作为金标准的队列中，SSAE显示出改进的F-measure为84.49%，精确率-召回率曲线下的平均面积（AveP）为78.83%。SSAE方法还优于其他九种最先进的核检测策略。

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net .

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We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.

我们实现并验证了一种用于胸部三维正电子发射断层扫描传输到计算机断层扫描配准的算法，使用互信息作为相似性准则。两种成像协议固有的差异导致两次采集之间存在显著的非刚性运动。采用刚性变形结合局部三次B样条来捕捉这种运动。变形定义在规则网格上，并由可能数千个系数参数化。结合基于样条的图像连续表示和Parzen直方图估计，我们的变形模型允许准则及其梯度的闭式表达式。在分层多分辨率框架中使用有限记忆拟牛顿优化算法自动对齐图像。为了表征该方法的性能，在验证研究中使用了来自常规肺癌分期患者的27次扫描。两位专家观察者使用分割窗口验证技术在特定解剖位置对配准进行视觉评估。视觉报告的误差在0至6毫米范围内，在中档性能工作站上的平均计算时间为100分钟。

In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development, we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine. The effectiveness of both methods is demonstrated through receiver operating characteristic analysis on two publicly available databases of color fundus images.

在计算机辅助诊断眼疾病的框架下，提出了一种基于线算子的视网膜血管分割方法。将先前用于乳腺摄影的线检测器应用于视网膜图像的绿色通道。该方法基于评估通过目标像素不同方向的固定长度直线的平均灰度值。考虑了两种分割方法。第一种使用基本线检测器，对其响应进行阈值化以获得无监督像素分类。作为进一步发展，我们采用两个正交线检测器以及目标像素的灰度值来构建特征向量，使用支持向量机进行监督分类。通过在两个公开的彩色眼底图像数据库上进行接收者操作特征分析，证明了两种方法的有效性。

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras (2000), we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.

我们提出了一种基于形状的曲线演化方法，用于分割包含已知物体类型的医学图像。具体来说，受Leventon、Grimson和Faugeras（2000）工作的启发，我们通过对训练数据的一组有符号距离表示进行主成分分析，推导出分割曲线隐式表示的参数模型。然后操作该表示的参数以最小化分割的目标函数。该算法能够处理多维数据，处理曲线的拓扑变化，对噪声和初始轮廓放置具有鲁棒性，并且计算效率高。同时，它避免了在算法训练阶段需要点对应。我们通过将该技术应用于两个医学应用来证明其有效性：心脏磁共振成像（MRI）的二维分割和前列腺MRI的三维分割。

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single “figure-of-merit” to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms.

图像标记区域的重叠测度，如Dice系数和Tanimoto系数，已被广泛用于评估图像配准和分割算法。现代研究可能包括在多个图像上定义的多个标签，然而大多数评估方案只报告每个标记区域的一个重叠值，简单地平均多个图像。在本文中，常见的重叠测度被推广到测量多个测试图像上定义的标签集合的总重叠，并使用模糊集理论处理分数标签。该框架允许报告单个“品质因数”，它通过图像对、标签或整体来总结复杂实验的结果。定义了一个互补的误差测度——重叠距离，它捕捉了非重叠部分的空间范围，并与在灰度图像上计算的Hausdorff距离相关。广义重叠测度在合成图像上进行了验证，这些图像的重叠可以通过解析计算，并作为三维磁共振成像（MRI）脑图像非刚性配准中的相似性度量。最后，通过将一个磁共振图谱脑配准到20个个体扫描，构建了一个实用的分割金标准，并与重叠测度一起用于评估公开可用的脑分割算法。

Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U-shape structure, we first design multiple global pyramid guidance (GPG) modules between the encoder and the decoder, aiming at providing different levels of global context information for the decoder by reconstructing skip-connection. We further design a scale-aware pyramid fusion (SAPF) module to dynamically fuse multi-scale context information in high-level features. These two pyramidal modules can exploit and fuse rich context information progressively. Experimental results show that our proposed method is very competitive with other state-of-the-art methods on four different challenging tasks, including skin lesion segmentation, retinal linear lesion segmentation, multi-class segmentation of thoracic organs at risk and multi-class segmentation of retinal edema lesions.

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When resampling an image to a new set of coordinates (for example, when rotating an image), there is often a noticeable loss in image quality. To preserve image quality, the interpolating function used for the resampling should be an ideal low-pass filter. To determine which limited extent convolving functions would provide the best interpolation, five functions were compared: A) nearest neighbor,...

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The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intra- and inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multi-center studies (more than 1000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.

磁共振成像（MRI）数据的定量分析在旨在研究人类大脑发育、功能和病理的研究及临床应用中日益重要。在药物疗效评估中，定量图像分析的作用不可避免地将增加，部分是由监管机构的要求所驱动。然而，从大型MRI数据库的手动分析中获得的测量结果耗时过长且存在显著的观察者内和观察者间变异性，这构成了定量MRI分析更广泛应用的主要障碍。我们开发了一个全自动的“流水线”图像分析框架，并已成功应用于多项大规模、多中心研究（超过1000次MRI扫描）。该流水线系统基于稳健的图像处理算法，以并行、分布式方式执行。本文描述了该系统在一项III期临床试验中自动量化多发性硬化病灶负荷的应用。通过广泛的验证研究评估了流水线结果，发现获得的病灶测量结果与经过训练的人类观察者获得的结果在统计学上无显著差异。由于自动分析消除了观察者内和观察者间的测量变异性，该系统增强了检测通过传统分析技术不易察觉的微小治疗效果的能力。虽然该流水线对多发性硬化的临床试验分析有用，但它具有应用于其他神经系统疾病以及一般神经生物学研究的广泛潜力。

In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246deg), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles

在动态磁共振成像（MRI）研究中，运动动力学或对比度变化往往难以预测，阻碍了对图像更新速率或时间分辨率的适当选择。基于黄金比例的恒定方位角轮廓间距（111.246°）被研究为径向MRI中从任意数量轮廓进行图像重建的最优方案。该轮廓顺序在信噪比（SNR）和伪影水平方面与均匀轮廓分布进行了评估和比较。该轮廓顺序的有利特性在两个健康志愿者的应用中得到了例证。首先，一种先进的滑动窗口重建方案应用于动态心脏成像，重建窗口可根据可接受的心脏运动程度灵活调整。其次，提出了一种对比度增强的k空间滤波器，允许从单个原始数据集在任意时间点重建任意数量的图像。该滤波器用于描绘单次反转预脉冲后大脑中的T1弛豫。虽然固定和预定轮廓数的均匀轮廓分布（恒定角度增量）是最优的，但基于黄金比例的轮廓分布被证明是任意数量轮廓的合适解决方案。

We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF. We use a new fibre ODF estimation obtained from a sharpening deconvolution transform (SDT) of the diffusion ODF reconstructed from q -ball imaging (QBI). This SDT provides new insight into the relationship between the HARDI signal, the diffusion ODF, and the fibre ODF. We demonstrate that the SDT agrees with classical spherical deconvolution and improves the angular resolution of QBI. Another important contribution of this paper is the development of new deterministic and new probabilistic tractography algorithms using the full multidirectional information obtained through use of the fibre ODF. An extensive comparison study is performed on human brain datasets comparing our new deterministic and probabilistic tracking algorithms in complex fibre crossing regions. Finally, as an application of our new probabilistic tracking, we quantify the reconstruction of transcallosal fibres intersecting with the corona radiata and the superior longitudinal fasciculus in a group of eight subjects. Most current diffusion tensor imaging (DTI)-based methods neglect these fibres, which might lead to incorrect interpretations of brain functions.

我们提出了一种用于描述基于高角分辨率扩散成像（HARDI）估计的纤维取向分布函数（ODF）的交叉和分裂纤维束的追踪综合概念。我们展示了为了进行准确的概率性纤维追踪，需要使用纤维ODF估计而不是扩散ODF。我们使用了从q-ball成像（QBI）重建的扩散ODF的锐化反卷积变换（SDT）获得的新纤维ODF估计。这种SDT提供了对HARDI信号、扩散ODF和纤维ODF之间关系的新见解。我们证明SDT与经典球面反卷积一致，并提高了QBI的角分辨率。本文的另一个重要贡献是利用通过纤维ODF获得的全多方向信息，开发了新的确定性和新的概率性纤维追踪算法。在人类脑数据集上进行了广泛的比较研究，比较了我们在复杂纤维交叉区域的新确定性和概率性追踪算法。最后，作为我们新的概率性追踪的应用，我们在八名受试者中量化了与放射冠和上纵束相交的跨胼胝体纤维的重建。目前大多数基于扩散张量成像（DTI）的方法忽略了这些纤维，这可能导致对脑功能的错误解释。

This paper presents two new rebinning algorithms for the reconstruction of three-dimensional (3-D) positron emission tomography (PET) data. A rebinning algorithm is one that first sorts the 3-D data into an ordinary two-dimensional (2-D) data set containing one sinogram for each transaxial slice to be reconstructed; the 3-D image is then recovered by applying to each slice a 2-D reconstruction method such as filtered-backprojection. This approach allows a significant speedup of 3-D reconstruction, which is particularly useful for applications involving dynamic acquisitions or whole-body imaging. The first new algorithm is obtained by discretizing an exact analytical inversion formula. The second algorithm, called the Fourier rebinning algorithm (FORE), is approximate but allows an efficient implementation based on taking 2-D Fourier transforms of the data. This second algorithm was implemented and applied to data acquired with the new generation of PET systems and also to simulated data for a scanner with an 18/spl deg/ axial aperture. The reconstructed images were compared to those obtained with the 3-D reprojection algorithm (3DRP) which is the standard "exact" 3-D filtered-backprojection method. Results demonstrate that FORE provides a reliable alternative to 3DRP, while at the same time achieving an order of magnitude reduction in processing time.

本文提出了两种新的重排算法，用于重建三维正电子发射断层扫描（PET）数据。重排算法首先将三维数据排序为普通二维数据集，其中包含每个待重建的横向切片的正弦图；然后通过对每个切片应用二维重建方法恢复三维图像...

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we propose Pathomic Fusion , an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNA-Seq) features for survival outcome prediction. Our approach models pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations, and controls the expressiveness of each representation via a gating-based attention mechanism. Following supervised learning, we are able to interpret and saliently localize features across each modality, and understand how feature importance shifts when conditioning on multimodal input. We validate our approach using glioma and clear cell renal cell carcinoma datasets from the Cancer Genome Atlas (TCGA), which contains paired whole-slide image, genotype, and transcriptome data with ground truth survival and histologic grade labels. In a 15-fold cross-validation, our results demonstrate that the proposed multimodal fusion paradigm improves prognostic determinations from ground truth grading and molecular subtyping, as well as unimodal deep networks trained on histology and genomic data alone. The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine heterogeneous data streams for understanding diseases and predicting response and resistance to treatment. Code and trained models are made available at: https://github.com/mahmoodlab/PathomicFusion .

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An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

提出了一种用于二维和三维多光谱磁共振图像模糊分割的算法，这些图像受到强度不均匀性（也称为阴影伪影）的污染。该算法是作者先前工作中提出的二维自适应模糊C均值算法的扩展。该算法将强度不均匀性建模为一个增益场，导致图像强度在图像空间中平滑且缓慢地变化。它迭代地适应强度不均匀性，并且是完全自动化的。在本文中，作者将二维自适应模糊C均值算法完全推广到三维多光谱图像。由于三维图像数据的潜在大小，他们还描述了一种新的基于多网格的更快算法来实现。他们使用模拟的磁共振数据表明，在分割受污染的图像时，三维自适应模糊C均值算法比标准的模糊C均值算法和其他两种竞争方法具有更低的错误率。使用真实的三维标量和多光谱磁共振脑图像进一步证明了其有效性。

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promisin...

将关于器官形状和位置的先验知识纳入是提高图像分析方法性能的关键。特别是，在图像因采集限制而损坏并包含伪影的情况下，先验知识非常有用。学习技术能够很好地捕捉解剖对象的高度约束特性。然而，在最近和最有希望...

In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major rol...

在计算机辅助诊断糖尿病视网膜病变的框架下，提出并讨论了一种检测渗出物的新算法。黄斑区内渗出物的存在是糖尿病性黄斑水肿的主要标志，可以高灵敏度地检测出来。因此，渗出物的检测是一项重要的诊断任务，计算机辅助在其中可能发挥主要作用。

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.

在计算机断层扫描（CT）图像上自动分割腹部解剖结构可以支持诊断、治疗计划制定和治疗实施流程。使用统计模型和多图谱标签融合（MALF）的分割方法需要个体间图像配准，这对腹部图像来说具有挑战性，而无配准的替代方法在大多数腹部器官上尚未达到更高精度。我们提出了一种免配准的深度学习分割算法，用于与内镜下胰腺和胆道手术导航相关的八个器官，包括胰腺、胃肠道（食管、胃和十二指肠）以及周围器官（肝脏、脾脏、左肾和胆囊）。我们在一个包含90名受试者的多中心数据集中，通过交叉验证直接将所提方法与现有深度学习和MALF方法的分割精度进行了比较。所提方法在所有器官上都获得了显著更高的Dice分数，并且在大多数器官上获得了更低的平均绝对距离，包括胰腺的Dice分数为0.78对比0.71、0.74和0.74，胃为0.90对比0.85、0.87和0.83，食管为0.76对比0.68、0.69和0.66。我们得出结论，基于深度学习的分割代表了一种用于多器官腹部CT分割的免配准方法，其精度可以超越当前方法，有可能支持胃肠道内镜手术中的图像引导导航。

Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.

结核病在全球许多地区构成重大健康威胁。免疫功能低下的HIV/AIDS患者中的机会性感染以及耐多药菌株加剧了这一问题，而结核病的诊断仍然具有挑战性。一旦未确诊因而未治疗，结核病患者的死亡率很高。标准诊断仍依赖于上世纪开发的方法，这些方法缓慢且常常不可靠。为减轻疾病负担，本文提出了一种在常规后前位胸部X光片中自动检测结核病的方法。我们首先使用图割分割方法提取肺部区域。针对该肺部区域，我们计算一组纹理和形状特征，从而使用二元分类器将X光片分为正常或异常。我们在两个数据集上测量系统的性能：一组由美国本地县卫生部门的结核病控制项目收集，另一组由中国深圳医院收集。所提出的用于结核病筛查的计算机辅助诊断系统已准备好进行现场部署，其性能接近人类专家的水平。我们在第一个数据集上实现了ROC曲线下面积（AUC）87%（准确率78.3%），在第二个数据集上实现了AUC 90%（准确率84%）。对于第一个数据集，我们将系统性能与放射科医生的性能进行了比较。在不遗漏任何阳性病例的情况下，放射科医生在该数据集上的准确率约为82%，其假阳性率约为我们系统的一半。

A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard technique for computing such descriptions is principal component analysis. However, principal component analysis is limited in that it only works for data lying in a Euclidean vector space. While this is certainly sufficient for geometric models that are parameterized by a set of landmarks or a dense collection of boundary points, it does not handle more complex representations of shape. We have been developing representations of geometry based on the medial axis description or m-rep. While the medial representation provides a rich language for variability in terms of bending, twisting, and widening, the medial parameters are not elements of a Euclidean vector space. They are in fact elements of a nonlinear Riemannian symmetric space. In this paper, we develop the method of principal geodesic analysis, a generalization of principal component analysis to the manifold setting. We demonstrate its use in describing the variability of medially-defined anatomical objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.

统计形状分析的一个主要目标是描述几何对象群体的变异性。标准技术是主成分分析。但主成分分析仅适用于欧几里得向量空间中的数据。对于由一组标志点或密集边界点参数化的几何模型，这足够，但不处理更复杂的形状表示。我们基于中轴描述（m-rep）开发了几何表示。中轴表示提供了弯曲、扭转、加宽等变异性丰富语言，但中轴参数不是欧几里得向量空间的元素，而是非线性黎曼对称空间的元素。本文发展了主测地线分析方法，将主成分分析推广到流形设置。我们展示了其在描述中轴定义的解剖对象变异性中的应用。在精神分裂症研究中，将该框架应用于海马群体的结果展示。

The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new metho...

数字病理学的出现为我们提供了自动分析病变组织全切片以获得可用于诊断和预后任务的定量图谱的挑战性机会。特别是，对于可解释模型的开发，细胞核的检测和分割至关重要。在本文中，我们描述了一种新的方法...

A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.53 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.

本文比较了六种基于强度的二维-三维（2-D-3-D）图像配准中的相似性度量。这些相似性度量的精度与使用基准标记精确计算的金标准配准进行了比较。这些相似性度量用于将脊柱模型的计算机断层扫描（CT）图像注册到荧光透视图像...

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages...

我们描述了一种自动定位眼底图像中视神经的方法。该方法使用一种我们称之为模糊收敛的新算法来确定血管网络的起点。我们使用31张健康视网膜图像和50张患病视网膜图像评估了该方法，这些图像包含各种症状，如迂曲血管、脉络膜新生血管和出血等。

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-...

我们提出了一种新颖的算法，用于从欠采样的k-t空间数据重建动态磁共振成像（MRI）数据。与依赖于傅里叶空间中稀疏性或带状结构的经典基于模型的电影MRI方案不同，我们利用数据在Karhunen-Loève变换（KLT）域中的紧凑表示来挖掘数据集中的相关性。使用数据-...

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

尽管诊断性医学成像在多种疾病的早期检测和准确诊断中提供了巨大益处，但人们对辐射诱导的遗传性、癌性及其他疾病的潜在副作用越来越担忧。如何在保持诊断性能的同时降低辐射剂量是计算机断层扫描（CT）领域的一个主要挑战。受压缩感知理论的启发，基于全变差（TV）最小化的稀疏约束已在低剂量CT重建中取得了有希望的结果。与TV方法中使用的离散梯度变换相比，字典学习被证明是一种有效的稀疏表示方式。另一方面，在低剂量CT情况下，考虑投影数据的统计特性也很重要。最近，我们开发了一种基于字典学习的低剂量X射线CT方法。本文详细介绍了该方法并通过实验进行了评估。在我们的方法中，将冗余字典的稀疏约束纳入统计迭代重建框架的目标函数中。字典可以在图像重建任务前预定义，也可以在重建过程中自适应定义。我们开发了一种交替最小化方案来最小化目标函数。我们使用动物和人类CT研究中收集的低剂量X射线投影来评估我们的方法，并相对于滤波反投影和基于TV的重建量化了字典学习的改进。结果表明，在我们选择的案例中，所提出的方法可能产生噪声更低、结构特征更详细的更好图像。然而，没有证据表明这对所有类型的结构都成立。