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Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient and has linear complexity with respect to the input. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the complexity. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BraTS, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while significantly reducing parameters and FLOPs by over 71%, compared to the best method in the literature. Our code and models are available at: https://tinyurl.com/2p87x5xn .

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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...

Based on the theory of approximation, this paper presents a unified analysis of interpolation and resampling techniques. An important issue is the choice of adequate basis functions. The authors show that, contrary to the common belief, those that perform best are not interpolating. By opposition to traditional interpolation, the authors call their use generalized interpolation; they involve a prefiltering step when correctly applied. The authors explain why the approximation order inherent in any basis function is important to limit interpolation artifacts. The decomposition theorem states that any basis function endowed with approximation order ran be expressed as the convolution of a B spline of the same order with another function that has none. This motivates the use of splines and spline-based functions as a tunable way to keep artifacts in check without any significant cost penalty. The authors discuss implementation and performance issues, and they provide experimental evidence to support their claims.

基于逼近理论，本文对插值和重采样技术进行了统一分析。一个重要问题是选择合适的基函数。作者表明，与普遍看法相反，表现最佳的基函数并非插值函数。与传统插值相反，作者称他们的使用为广义插值；当正确应用时，它涉及一个预滤波步骤。作者解释了任何基函数固有的逼近阶对于限制插值伪影的重要性。分解定理指出，任何具有逼近阶的基函数都可以表示为相同阶的B样条与另一个没有逼近阶的函数的卷积。这促使使用样条和基于样条的函数作为一种可调谐的方式来控制伪影，而无需显著增加成本。作者讨论了实现和性能问题，并提供了实验证据来支持他们的主张。

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

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 the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it d...

我们提出了用于断层重建的学习型原始-对偶算法。该算法通过展开近端原始-对偶优化方法，将（可能非线性的）前向算子纳入深度神经网络中，其中近端算子被卷积神经网络取代。该算法以端到端方式训练，直接处理原始测量数据，并且...

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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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……

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。

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...

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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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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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。

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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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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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。

The forward electroencephalography (EEG) problem involves finding a potential V from the Poisson equation /spl nabla//spl middot/(/spl sigma//spl nabla/V)=f, in which f represents electrical sources in the brain, and /spl sigma/ the conductivity of the head tissues. In the piecewise constant conductivity head model, this can be accomplished by the boundary element method (BEM) using a suitable integral formulation. Most previous work uses the same integral formulation, corresponding to a double-layer potential. We present a conceptual framework based on a well-known theorem (Theorem 1) that characterizes harmonic functions defined on the complement of a bounded smooth surface. This theorem says that such harmonic functions are completely defined by their values and those of their normal derivatives on this surface. It allows us to cast the previous BEM approaches in a unified setting and to develop two new approaches corresponding to different ways of exploiting the same theorem. Specifically, we first present a dual approach which involves a single-layer potential. Then, we propose a symmetric formulation, which combines single- and double-layer potentials, and which is new to the field of EEG, although it has been applied to other problems in electromagnetism. The three methods have been evaluated numerically using a spherical geometry with known analytical solution, and the symmetric formulation achieves a significantly higher accuracy than the alternative methods. Additionally, we present results with realistically shaped meshes. Beside providing a better understanding of the foundations of BEM methods, our approach appears to lead also to more efficient algorithms.

前向脑电图问题涉及从泊松方程∇·(σ∇V)=f中求解电位V，其中f代表脑内的电源，σ代表头部组织的电导率。在分段恒定电导率头部模型中，可以通过边界元法使用合适的积分公式来实现。以往的大多数工作使用相同的积分公式，对应于双层电位。我们提出了一个基于一个著名定理（定理1）的概念框架，该定理描述了定义在有界光滑曲面补集上的调和函数的特征。该定理指出，这些调和函数完全由它们在曲面上的值及其法向导数的值确定。它使我们能够将先前的边界元方法统一在一个框架中，并开发出两种利用该定理的新方法。具体地，我们首先提出了一种涉及单层电位的对偶方法。然后，我们提出了一种对称公式，它结合了单层和双层电位，这对脑电图领域来说是新颖的，尽管它已应用于电磁学中的其他问题。使用已知解析解的球形几何体对这三种方法进行了数值评估，对称公式的精度显著高于其他方法。此外，我们展示了使用真实形状网格的结果。除了更好地理解边界元方法的基础外，我们的方法似乎也导致了更高效的算法。

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。

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...

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...

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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