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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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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。

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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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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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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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。

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...

Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.

前列腺癌是西方世界男性癌症死亡的主要原因之一。磁共振成像（MRI）作为一种检测前列腺癌的模态正被越来越多地使用。因此，在MRI图像中计算机辅助检测前列腺癌已成为一个活跃的研究领域。在本文中，我们研究了一个全自动的计算机辅助检测系统，该系统包括两个阶段。在第一阶段，我们使用基于多图谱的前列腺分割、体素特征提取、分类和局部最大值检测来检测初始候选点。第二阶段对候选区域进行分割，并通过分类获得每个候选点的癌症可能性。特征包括药代动力学行为、对称性和外观等。该系统在一个包括347名患者的大规模连续队列上进行评估，以MR引导活检作为参考标准。该队列包含165名癌症患者和182名非前列腺癌患者。性能评估基于病灶的自由响应受试者工作特征曲线和基于患者的受试者工作特征分析。该系统还与放射科医生的前瞻性临床表现进行了比较。结果显示，在每个正常病例中，假阳性率为0.1、1和10时，灵敏度分别为0.42、0.75和0.89。在临床工作流程中，该系统可能用于提高放射科医生的灵敏度。在典型筛查情境的高特异性读数设置下，该系统的表现与放射科医生无显著差异，并且可以用作独立的第二读者，而不是第二放射科医生。此外，该系统在第一读者设置中也有潜力。

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver...

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We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvo...

我们提出了一种基于具有捷径连接的深度3D卷积编码器网络的新颖分割方法，并将其应用于磁共振图像中多发性硬化（MS）病灶的分割。我们的模型是一个由两个相互连接的路径组成的神经网络：一个卷积路径，学习越来越抽象和更高层次的图像特征；以及一个反卷积路径，该路径基于卷积编码器提取的多尺度特征生成精确的分割图。捷径连接将两个路径对称连接。我们在MS病灶分割任务上评估了我们的方法，并展示了相对于几种最新方法的显著改进。

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

结肠镜检查是结肠癌筛查的金标准，但有些息肉仍会被漏检，从而阻碍了疾病的早期发现和治疗。人们提出了几种计算机辅助系统来帮助结肠镜检查中的息肉检测，但至今缺乏一致的评估。公开可用的标注数据库的缺乏使得难以比较不同方法，并评估它们是否达到临床可接受的性能水平。作为2015年国际医学图像计算和计算机辅助介入会议（MICCAI）上内窥镜视觉挑战赛（http://endovis.grand-challenge.org）的一部分，自动息肉检测子挑战赛旨在解决这一需求。在本文中，我们报告了这项息肉检测方法比较评估的结果，并描述了进一步探索方法间差异的附加实验。我们定义了性能指标并提供了允许比较多种方法的评估数据库。结果表明，卷积神经网络是目前最先进的技术。尽管如此，也证明了结合不同方法可以提高整体性能。

An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and Taylor, 1995, 1999, and 2001]. A nonlinear kNN-classifier is used, instead of the linear Mahalanobis distance, to find optimal displacements for landmarks. For each of the landmarks that describe the shape, at each resolution level taken into account during the segmentation optimization procedure, a distinct set of optimal features is determined. The selection of features is automatic, using the training images and sequential feature forward and backward selection. The new approach is tested on synthetic data and in four medical segmentation tasks: segmenting the right and left lung fields in a database of 230 chest radiographs, and segmenting the cerebellum and corpus callosum in a database of 90 slices from MRI brain images. In all cases, the new method produces significantly better results in terms of an overlap error measure (p<0.001 using a paired T-test) than the original active shape model scheme.

提出了一种由最优局部特征引导的主动形状模型分割方案，与原始公式（Cootes和Taylor，1995、1999和2001）中使用的归一化一阶导数轮廓相反。使用非线性kNN分类器代替线性马氏距离来寻找地标的最优位移。对于描述形状的每个地标，在分割优化过程中考虑的每个分辨率级别上，确定一组不同的最优特征。特征是自动选择的，利用训练图像和顺序特征前向和后向选择。新方法在合成数据和四个医学分割任务中进行了测试：在230张胸部X光片数据库中分割左右肺野，以及在90张MRI脑图像切片数据库中分割小脑和胼胝体。在所有情况下，新方法在重叠误差度量方面（配对T检验p<0.001）比原始主动形状模型方案产生显著更好的结果。

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.

在数字彩色眼底照片中稳健地检测红色病灶是开发糖尿病视网膜病变自动化筛查系统的关键步骤。本文提出了一种基于混合方法的新型红色病灶检测方法，结合了Spencer等人(1996)和Frame等人(1998)的前期工作，并有两项重要的新贡献。第一个贡献是基于像素分类的新的红色病灶候选检测系统。利用该技术，血管和红色病灶从图像背景中分离出来。在移除连接的血管后，剩余的对象被认为是可能的红色病灶。其次，在Spencer-Frame提出的特征基础上增加了大量新特征。使用所有特征和k近邻分类器对检测到的候选对象进行分类。在由筛查集中常见图像组成的测试集上进行了广泛评估。在判断图像是否包含红色病灶时，该系统在特异性为87%的情况下达到了100%的敏感性。该方法与几种不同的自动化系统进行了比较，并显示出优于它们的结果。性能接近于人类专家检查图像中是否存在红色病灶的表现。

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.

视盘检测是开发各种严重眼科病理自动诊断系统的重要步骤。本文提出了一种新的基于模板的方法，用于从数字视网膜图像中分割视盘。该方法使用形态学和边缘检测技术，然后进行圆形霍夫变换以获得近似的圆形视盘边界。它需要视盘内的一个像素作为初始信息。为此，还提出了一种基于投票类型算法的定位方法。在公开的MESSIDOR数据库的1200张图像上评估了这些算法。定位程序在99%的情况下成功，平均计算时间为1.67秒，标准差为0.14秒。另一方面，分割算法在自动分割与真实视盘区域之间的平均共同区域重叠率为86%。平均计算时间为5.69秒，标准差为0.54秒。此外，本文还讨论了更常用于视盘分割的模型的优缺点。

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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Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.

磁共振成像越来越被用于评估婴儿的大脑生长和发育。这类研究通常基于对脑部MRI图像解剖分割的定量分析。然而，与发育相关的大脑形状和外观的巨大变化、新生儿大脑中较低的信噪比和部分容积效应，给新生儿MRI数据的自动分割带来了挑战。在这项研究中，我们提出了一个框架，用于对发育中的新生儿大脑（从早期早产期到足月等效年龄）进行精确的基于强度的分割，将其划分为50个脑区。我们提出了一种新颖的分割算法，通过引入结构层次和解剖约束来建模整个大脑的强度。将该方法与标准图谱基技术进行比较，并改进与手动参考分割的标签重叠。我们证明，该技术实现了高度精确的结果，并且在从24周胎龄到足月等效年龄的广泛胎龄范围内非常稳健。

We describe a method for automatically building statistical shape models from a training set of example boundaries/surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of "landmarks" manually on each training image, which is time consuming and subjective in two dimensions and almost impossible in three dimensions. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the "best" model. We define "best" as that which minimizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard. We also show that the method can be extended straightforwardly to three dimensions.

我们描述了一种从示例边界/表面训练集自动构建统计形状模型的方法。这些模型作为分割和解释图像的基础显示出相当大的前景。然而，该方法的一个缺点是需要在一组训练形状的所有成员之间建立一组密集的对应关系。这通常是通过在每个训练图像上手动定位一组“地标”来实现的，这在二维中耗时且主观，在三维中几乎不可能。我们描述了如何通过将对应问题转化为寻找训练集中每个形状的参数化来自动构建形状模型。我们选择构建“最佳”模型的参数化集合。我们将“最佳”定义为最小化训练集描述长度的参数化，认为这会导致模型具有良好的紧凑性、特异性和泛化能力。我们展示了如何表示和操作一组形状参数化以构建最小描述长度模型。给出了几个不同二维边界训练集的结果，表明所提出的方法构建的模型优于其他方法，包括当前金标准的手动地标定位。我们还表明该方法可以直接扩展到三维。

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

传统的胸部X线照片在临床实践中仍然无处不在，并且很可能在相当长的一段时间内保持这种状态。然而，其解读是出了名的困难。这解释了对计算机辅助诊断在胸部X线摄影中持续的兴趣。本综述的目的是对关于胸部图像计算机分析的文献进行分类和简要回顾，这包括过去30年发表的150多篇论文。指出了剩余的挑战，并给出了一些未来研究的方向。

This paper presents a robust and fully automatic filter-based approach for retinal vessel segmentation. We propose new filters based on 3D rotating frames in so-called orientation scores, which are functions on the Lie-group domain of positions and orientations ℝ 2 × S 1 . By means of a wavelet-type transform, a 2D image is lifted to a 3D orientation score, where elongated structures are disentangled into their corresponding orientation planes. In the lifted domain ℝ 2 × S 1 , vessels are enhanced by means of multi-scale second-order Gaussian derivatives perpendicular to the line structures. More precisely, we use a left-invariant rotating derivative (LID) frame, and a locally adaptive derivative (LAD) frame. The LAD is adaptive to the local line structures and is found by eigensystem analysis of the left-invariant Hessian matrix (computed with the LID). After multi-scale filtering via the LID or LAD in the orientation score domain, the results are projected back to the 2D image plane giving us the enhanced vessels. Then a binary segmentation is obtained through thresholding. The proposed methods are validated on six retinal image datasets with different image types, on which competitive segmentation performances are achieved. In particular, the proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods. The LAD-OS is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels. The high computational speed of the proposed methods allows processing of large datasets in a screening setting.

本文提出了一种鲁棒且全自动的基于滤波器的视网膜血管分割方法。我们提出了基于所谓方向得分中的3D旋转框架的新滤波器，方向得分是位置和方向李群域ℝ²×S¹上的函数。通过小波型变换，将2D图像提升到3D方向得分，其中细长结构被分解到其对应的方向平面中。在提升域ℝ²×S¹中，通过垂直于线结构的多尺度二阶高斯导数增强血管。更精确地说，我们使用了左不变旋转导数（LID）框架和局部自适应导数（LAD）框架。LAD对局部线结构自适应，通过左不变Hessian矩阵（使用LID计算）的特征系统分析得到。通过在方向得分域中使用LID或LAD进行多尺度滤波后，结果投影回2D图像平面，得到增强的血管。然后通过阈值化得到二值分割。所提出的方法在六个具有不同图像类型的视网膜图像数据集上进行了验证，取得了具有竞争力的分割性能。特别是，所提出的在方向得分上应用LAD滤波器（LAD-OS）的算法优于大多数现有方法。LAD-OS能够处理典型困难情况，如交叉、中央动脉反射、紧密平行和微小的血管。所提出的方法计算速度快，允许在筛选设置中处理大型数据集。

Bone age rating is associated with a considerable variability from the human interpretation, and this is the motivation for presenting a new method for automated determination of bone age (skeletal maturity). The method, called BoneXpert, reconstructs, from radiographs of the hand, the borders of 15 bones automatically and then computes ldquointrinsicrdquo bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, it transforms the intrinsic bone ages into Greulich Pyle (GP) or Tanner Whitehouse (TW) bone age. The bone reconstruction method automatically rejects images with abnormal bone morphology or very poor image quality. From the methodological point of view, BoneXpert contains the following innovations: 1) a generative model (active appearance model) for the bone reconstruction; 2) the prediction of bone age from shape, intensity, and texture scores derived from principal component analysis; 3) the consensus bone age concept that defines bone age of each bone as the best estimate of the bone age of the other bones in the hand; 4) a common bone age model for males and females; and 5) the unified modelling of TW and GP bone age. BoneXpert is developed on 1559 images. It is validated on the Greulich Pyle atlas in the age range 2-17 years yielding an SD of 0.42 years [0.37; 0.47] 95% conf, and on 84 clinical TW-rated images yielding an SD of 0.80 years [0.68; 0.93] 95% conf. The precision of the GP bone age determination (its ability to yield the same result on a repeated radiograph) is inferred under suitable assumptions from six longitudinal series of radiographs. The result is an SD on a single determination of 0.17 years [0.13; 0.21] 95% conf.

骨龄评分与人类解释存在相当大的变异性，这促使我们提出一种新的自动确定骨龄（骨骼成熟度）的方法。该方法称为BoneXpert，从手部X光片自动重建15块骨的边界，然后计算13块骨（桡骨、u...）的“内在”骨龄。

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentatio...

乳腺X线摄影风险评分通常通过从乳腺X线照片中提取一组手工特征，并将响应直接或间接与乳腺癌风险相关联来实现自动化。我们提出了一种从未标记数据中学习特征层次结构的方法。当学习到的特征被用作简单分类器的输入时，可以解决两个不同的任务：i）乳腺密度分割...

We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans—with manually segmented white matter, cerebral cortex, ventricles and subcortical structures—to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

我们提出了一种非参数、概率模型，用于在给定一组训练图像和对应标签图的情况下自动分割医学图像。由此产生的推理算法依赖于测试图像与单个训练图像之间的成对配准。然后将训练标签转移到测试图像并融合，以计算测试对象的最终分割。这种标签融合方法已被证明能产生准确的分割，因为使用多个配准能够捕获更大的受试者间解剖变异性，并提高对偶发配准失败的鲁棒性。据我们所知，本文首次提出了一个全面的概率框架，严格地证明了标签融合作为一种分割方法的合理性。所提出的框架使我们能够在理论上和实践上比较不同的标签融合算法。特别是，最近的标签融合或多图谱分割算法被解释为我们框架的特例。我们进行了两组实验来验证所提出的方法。在第一组实验中，我们使用39个脑部MRI扫描（具有手动分割的白质、大脑皮层、脑室和皮层下结构）来比较不同的标签融合算法和广泛使用的FreeSurfer全脑分割工具。我们的结果表明，所提出的框架比FreeSurfer和之前的标签融合算法产生更准确的分割。在第二个实验中，我们使用282名受试者的脑部MRI扫描，证明所提出的分割工具足够敏感，能够在衰老和阿尔茨海默病的研究中稳健地检测海马体积变化。

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimate...

本文提出一种全自动算法，用于从多光谱磁共振（MR）图像中分割多发性硬化（MS）病灶。该方法利用正常脑图像的随机模型进行基于强度的组织分类，同时将模型中无法很好解释的异常点检测为MS病灶。它校正了MR场不均匀性，并估计...

Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important for segmentation task to build global context dependencies. The Transformers can establish long-range dependencies among pixels by self-attention, providing a supplement to the local convolution. In addition, multi-scale feature fusion and feature selection are crucial for medical image segmentation tasks, which is ignored by Transformers. However, it is challenging to directly apply self-attention to CNNs due to the quadratic computational complexity for high-resolution feature maps. Therefore, to integrate the merits of CNNs, multi-scale channel attention and Transformers, we propose an efficient hierarchical hybrid vision Transformer (H2Former) for medical image segmentation. With these merits, the model can be data-efficient for limited medical data regime. The experimental results show that our approach exceeds previous Transformer, CNNs and hybrid methods on three 2D and two 3D medical image segmentation tasks. Moreover, it keeps computational efficiency in model parameters, FLOPs and inference time. For example, H2Former outperforms TransUNet by 2.29% in IoU score on KVASIR-SEG dataset with 30.77% parameters and 59.23% FLOPs.

中文摘要翻译待生成