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In magnetic resonance imaging (MRI), the spatial inhomogeneity of the static magnetic field can cause degraded images if the reconstruction is based on inverse Fourier transformation. This paper presents and discusses a range of fast reconstruction algorithms that attempt to avoid such degradation by taking the field inhomogeneity into account. Some of these algorithms are new, others are modified...

在磁共振成像（MRI）中，如果重建基于逆傅里叶变换，静态磁场的空间不均匀性可能导致图像质量下降。本文介绍并讨论了一系列快速重建算法，这些算法通过考虑场不均匀性来避免这种退化。其中一些算法是新的，另一些则是经过改进的...

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...

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. Following recent developments in compressive sensing (CS) theory, several authors have demonstrated that certain classes of MR images which possess sparse representations in some transform domain can be accurately reconstructed from very highly undersampled K -space data by solving a convex lscr 1 -minimization problem. Although lscr 1 -based techniques are extremely powerful, they inherently require a degree of over-sampling above the theoretical minimum sampling rate to guarantee that exact reconstruction can be achieved. In this paper, we propose a generalization of the CS paradigm based on homotopic approximation of the lscr 0 quasi-norm and show how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound. Following a brief review of standard CS methods and the developed theoretical extensions, several example MRI reconstructions from highly undersampled K -space data are presented.

在临床磁共振成像（MRI）中，扫描时间的任何减少都能带来诸多潜在好处，从高时间速率观察生理过程到提高患者舒适度。随着压缩感知（CS）理论的最新发展，多位作者已经证明，通过求解一个凸的ℓ1最小化问题，可以从高度欠采样的K空间数据中准确重建出在某些变换域中具有稀疏表示的特定类别的MR图像。尽管基于ℓ1的技术非常强大，但它们本质上需要高于理论最小采样率的过采样程度，以确保能够实现精确重建。在本文中，我们提出了一种基于ℓ0拟范数同伦近似的CS范式的推广，并展示了如何将MR图像重建进一步推至奈奎斯特极限以下，并显著接近理论界限。在简要回顾标准CS方法和所发展的理论扩展之后，我们展示了几个从高度欠采样的K空间数据进行MRI重建的示例。

This paper outlines a simple, fast, and accurate method for automatically locating the nipple on digitized mammograms that have been segmented to reveal the skin-air interface. If the average gradient of the intensity is computed in the direction normal to the interface and directed inside the breast, it is found that there is a sudden and distinct change in this parameter close to the nipple. A n...

本文概述了一种简单、快速且准确的方法，用于在已分割以显示皮肤-空气界面的数字化乳腺X线照片上自动定位乳头。如果计算垂直于界面方向并指向乳房内部的平均强度梯度，就会发现该参数在乳头附近发生突然而明显的变化。一

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。

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-r...

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Algebraic reconstruction techniques (ART) are iterative procedures for recovering objects from their projections. It is claimed that by a careful adjustment of the order in which the collected data are accessed during the reconstruction procedure and of the so-called relaxation parameters that are to be chosen in an algebraic reconstruction technique, ART can produce high-quality reconstructions w...

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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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Presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. The authors' goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter ...

针对磁共振（MR）数据分割中的混合模型，提出了两种新的鲁棒参数估计方法。头部由不同类型的组织构成，这些组织可以用多元高斯分布的有限混合来建模。作者的目标是在存在其他不太感兴趣的组织的情况下，准确估计所需组织的统计量。这些后者……

Methods of quantitative emission computed tomography require compensation for linear photon attenuation. A current trend in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) is to employ transmission scanning to reconstruct the attenuation map. Such an approach, however, considerably complicates both the scanner design and the data acquisition protocol. A dr...

定量发射计算机断层扫描方法需要对线性光子衰减进行补偿。当前单光子发射计算机断层扫描（SPECT）和正电子发射断层扫描（PET）的一个趋势是使用透射扫描来重建衰减图。然而，这种方法大大复杂化了扫描仪设计和数据采集协议。一个...

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

我们提出了一种用于医学图像噪声过滤的鲁棒小波域方法。该方法能够自适应地处理各种类型的图像噪声，并满足医学专家的偏好；通过单个参数，可以在保留（依赖于专家的）相关细节和降噪程度之间取得平衡。该算法利用关于显著图像特征在不同分辨率尺度上相关性的普遍有效知识，进行初步的系数分类。这种初步的系数分类用于经验性地估计代表有用图像特征的系数和主要代表噪声的系数的统计分布。通过使用局部空间活动的小波域指示器来实现对图像空间上下文的适应。该方法在实现和执行时间上都具有较低的复杂度。结果证明了它在医学超声和磁共振成像中噪声抑制的有效性。在这些应用中，无论在定量性能指标还是图像视觉质量方面，该方法都明显优于单分辨率空间自适应算法。

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。

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME....

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Respiratory motion during the collection of computed tomography (CT) projections generates structured artifacts and a loss of resolution that can render the scans unusable. This motion is problematic in scans of those patients who cannot suspend respiration, such as the very young or intubated patients. Here, the authors present an algorithm that can be used to reduce motion artifacts in CT scans ...

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Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoisin...

低剂量计算机断层扫描（LDCT）在医学成像领域引起了广泛关注，因为CT相关的X射线辐射对患者存在健康风险。然而，降低CT辐射剂量会降低信噪比，从而影响图像质量和诊断性能。最近，基于深度学习的算法在LDCT去噪方面取得了令人鼓舞的结果。

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。

In this paper implicit representations of deformable models for medical image enhancement and segmentation are considered. The advantage of implicit models over classical explicit models is that their topology can be naturally adapted to objects in the scene. A geodesic formulation of implicit deformable models is especially attractive since it has the energy minimizing properties of classical mod...

本文考虑用于医学图像增强和分割的可变形模型的隐式表示。隐式模型相对于经典显式模型的优势在于其拓扑结构可以自然地适应场景中的物体。隐式可变形模型的测地线公式尤其吸引人，因为它具有经典模型的能量最小化特性……

The authors have applied mathematical morphology for fractal analysis on bone X-ray images. The digitized gray level image is treated as a three-dimensional surface whose fractal dimension is calculated by performing a series of dilations on this surface and plotting the area of the resulting set of surfaces against the size of the structuring element. This approach has the added advantage of enco...

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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。

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters a...

压缩感知（CS）已被证明对于从稀疏采集数据或欠采样测量中进行断层重建是有效的，这对于少视图计算机断层扫描（CT）、断层合成、内部断层成像等实际应用非常重要。为了执行稀疏数据CT，迭代重建通常使用CS框架中的正则化器。目前，如何选择参数...

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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It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori inf...

众所周知，二元分类器有两个隐含的目标函数（灵敏度和特异度）来描述其性能。传统的分类器训练方法试图将这两个目标函数（或两个类似的类别性能度量）合并为一个，以便使用传统的标量优化技术。这涉及引入先验信息...

The authors have devised, implemented, and tested a fast, spatially accurate technique for calculating the high-dimensional deformation field relating the brain anatomies of an arbitrary pair of subjects. The resulting three-dimensional (3-D) deformation map can be used to quantify anatomic differences between subjects or within the same subject over time and to transfer functional information bet...

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In many medical imaging and classical computer vision tasks, the Dice score and Jaccard index are used to evaluate the segmentation performance. Despite the existence and great empirical success of metric-sensitive losses, i.e. relaxations of these metrics such as soft Dice, soft Jaccard and Lovász-Softmax, many researchers still use per-pixel losses, such as (weighted) cross-entropy to train CNNs for segmentation. Therefore, the target metric is in many cases not directly optimized. We investigate from a theoretical perspective, the relation within the group of metric-sensitive loss functions and question the existence of an optimal weighting scheme for weighted cross-entropy to optimize the Dice score and Jaccard index at test time. We find that the Dice score and Jaccard index approximate each other relatively and absolutely, but we find no such approximation for a weighted Hamming similarity. For the Tversky loss, the approximation gets monotonically worse when deviating from the trivial weight setting where soft Tversky equals soft Dice. We verify these results empirically in an extensive validation on six medical segmentation tasks and can confirm that metric-sensitive losses are superior to cross-entropy based loss functions in case of evaluation with Dice Score or Jaccard Index. This further holds in a multi-class setting, and across different object sizes and foreground/background ratios. These results encourage a wider adoption of metric-sensitive loss functions for medical segmentation tasks where the performance measure of interest is the Dice score or Jaccard index.

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