Most Cited Articles

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

中文摘要翻译待生成

A new implementation of the minimum spanning tree (MST) phase unwrapping method is presented. The time complexity of the MST method is reduced from O(n/sup 2/) to O(n log/sub 2/ n), where n is the number of pixels in the phase map. Typical 256/spl times/256 phase maps from magnetic resonance imaging can be unwrapped in seconds, compared with tens of minutes with the O(n/sup 2/) implementation. Thi...

提出了一种新的最小生成树（MST）相位展开方法的实现。该方法的时间复杂度从O(n²)降低到O(n log₂ n)，其中n是相位图中的像素数。典型的256×256磁共振相位图可以在几秒内展开，而O(n²)的实现则需要几十分钟。

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

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

In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approx...

本文应用时间学习-滤波程序来优化由主动轮廓模型检测到的左心室（LV）边界。不是对LV形状或运动做出先验假设，而是直接从图像中逐步收集这些信息，并利用它们实现更一致的分割。使用霍夫变换技术来找到初始近似...

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。

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

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

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

For newly developed iterative Newton-Kantorovitch reconstruction techniques, the quality of the final image depends on both experimental and model noise. Experimental noise is inherent to any experimental acquisition scheme, while model noise refers to the accuracy of the numerical model, used in the reconstruction process, to reproduce the experimental setup. This paper provides a systematic asse...

对于新发展的迭代牛顿-坎托罗维奇重建技术，最终图像的质量取决于实验噪声和模型噪声。实验噪声是任何实验采集方案固有的，而模型噪声指的是重建过程中用于再现实验设置的数值模型的精度。本文提供了一种系统的评估...

The gain achieved in image quality by utilizing, in the image forming process, the time-of-flight information (TOF) of positron annihilation photons between their inception and detection was measured experimentally by means of a positron emission tomograph (PET)-Super PETT I. The measurements were carried out by imaging a 35 cm cylindrical uniform phantom containing different positron activity con...

中文摘要翻译待生成

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。

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

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

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...

中文摘要翻译待生成

A statistical method for detecting activated pixels in functional MRI (fMRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patte...

提出了一种用于检测功能磁共振成像（fMRI）数据中激活像素的统计方法。在该方法中，每个像素处的fMRI时间序列被建模为响应信号（由实验控制的激活-基线模式引起）、不感兴趣的干扰效应和高斯白噪声之和。对于周期性激活-基线模式...

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

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

Localization ROC (LROC) observer studies examined whether detector response compensation (DRC) in ordered-subset, expectation-maximization (OSEM) reconstructions helps in the detection and localization of hot tumors. Simulated gallium (Ga-67) images of the thoracic region were used in the study. The projection data modeled the acquisition of attenuated 93- and 185-keV photons with a medium-energy ...

局部化ROC（LROC）观察者研究考察了在有序子集期望最大化（OSEM）重建中进行探测器响应补偿（DRC）是否有助于检测和定位热肿瘤。研究中使用了模拟的胸部区域镓（Ga-67）图像。投影数据模拟了中能准直器下93 keV和185 keV衰减光子的采集。

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.

中文摘要翻译待生成

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

中文摘要翻译待生成

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

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

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。

Cochlear implantation is the standard treatment for profound hearing loss, Preimplantation and postimplantation spiral computed tomography (CT) is essential in several key clinical and research aspects. The maximum image resolution with commercial spiral CT scanners is insufficient to define clearly anatomical features and implant electrode positions in the inner ear, In this paper, the authors de...

人工耳蜗植入是治疗重度听力损失的标准方法，术前和术后螺旋计算机断层扫描（CT）在多个关键的临床和研究方面至关重要。商用螺旋CT扫描仪的最大图像分辨率不足以清晰定义内耳的解剖结构和植入电极位置。在本文中，作者...

The authors present a new algorithm to enhance the edges and contrast of chest and breast radiographs while minimally amplifying image noise. The algorithm consists of a linear combination of an original image and two smoothed images obtained from it by using different masks and parameters, followed by the application of nonlinear contrast stretching. The result is an image which retains the high ...

中文摘要翻译待生成

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

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

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。

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。

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

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