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A technique is presented for automatic detection of the longitudinal fissure in tomographic scans of the brain. The technique utilizes the planar nature of the fissure and is a three-dimensional variant of the Hough transform principle. Algorithmic and computational aspects of the technique are discussed. Results and performance on coronal and transaxial magnetic resonance data show that the algor...

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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。

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。

Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performan...

基于活动模型的图像分割在医学图像处理中经常被使用，并取得了相当大的成功。尽管基于活动模型的方法最初被视为优化问题，但大多数研究人员将其实现为偏微分方程解。基于活动模型的方法的优缺点很明显：速度和稳定性。为了提高其性能...

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。

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。

The problem of reconstruction in positron emission tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of the maximum-likelihood (ML) algorithm, the problem of reconstruction is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter dens...

中文摘要翻译待生成

Large-tip-angle multidimensional radio-frequency (RF) pulse design is a difficult problem, due to the nonlinear response of magnetization to applied RF at large tip-angles. In parallel excitation, multidimensional RF pulse design is further complicated by the possibility for transmit field patterns to change between subjects, requiring pulses to be designed rapidly while a subject lies in the scan...

大翻转角多维射频（RF）脉冲设计是一个难题，因为在大翻转角下磁化对施加RF的响应是非线性的。在并行激励中，由于发射场模式可能在不同受试者之间变化，需要受试者在扫描过程中快速设计脉冲，这使得多维RF脉冲设计更加复杂。

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。

Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works ...

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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。

We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet se...

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A new imaging modality is introduced to image electrical conductivity of biological tissues via contactless measurements. This modality uses magnetic excitation to induce currents inside the body and measures the magnetic fields of the induced currents. In this study, the mathematical basis of the methodology is analyzed and numerical models are developed to simulate the imaging system. The induce...

介绍了一种新的成像模态，通过非接触测量对生物组织的电导率进行成像。该模态利用磁激励在体内感应电流，并测量感应电流产生的磁场。本研究分析了该方法的数学基础，并开发了数值模型来模拟成像系统。感应...

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...

The Fourier inversion method for reconstruction of images in computerized tomography has not been widely used owing to the perceived difficulty of interpolating from polar or other measurement grids to the Cartesian grid required for fast numerical Fourier inversion. Although the Fourier inversion method is recognized as being computationally faster than the back-projection method for parallel ray...

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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。

Fractal analysis of bone X-ray images has received much interest recently for the diagnosis of bone disease. Here, the authors propose a fractal analysis of bone X-ray tomographic microscopy (XTM) projections. The aim of the study is to establish whether or not there is a correlation between three-dimensional (3-D) trabecular changes and two-dimensional (2-D) fractal descriptors. Using a highly co...

骨X射线图像的分形分析近来在骨病诊断中受到广泛关注。本文作者提出对骨X射线断层显微镜（XTM）投影进行分形分析。研究旨在确定三维（3-D）小梁变化与二维（2-D）分形描述符之间是否存在相关性。利用高度共...

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...

Considers the direct Fourier methods (DFM's) for reconstructing an image from its given X-ray projections. The main purpose here is to use concepts from numerical analysis to estimate the errors in the methods. The authors also suggest an alternative to the interpolations involved in the DFM's and estimate the number of terms involved. Realizing that one of the main reasons for the degradation of ...

考虑使用直接傅里叶方法（DFM）从给定的X射线投影重建图像。这里的主要目的是使用数值分析的概念来估计方法中的误差。作者还提出了DFM中涉及的插值的替代方案，并估计了涉及的项数。意识到...

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。

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。

Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection respo...

准确检测血管边界对于磁共振血管成像（MRA）中血管的精确提取尤为重要。本文提出使用基于加权局部方差（WLV）的边缘检测方案用于MRA中的血管边界检测。该方法对边缘强度对比度的变化具有鲁棒性，并且能够给出高检测响应...

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-...

A new method to reconstruct the elastic modulus of soft tissue subjected to an external static compression is presented. In this approach the Newton-Raphson method is used to vary a finite element (FE) model of the elasticity equations to fit, in a least squared sense, a set of axial tissue displacement fields estimated using a correlation technique applied to ultrasound signals. The ill-condition...

中文摘要翻译待生成

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。