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A projection operator onto a closed convex set in Hilbert space is one of the few examples of a nonlinear map that can be defined in simple abstract terms. Moreover, it minimizes distance and is nonexpansive, and therefore shares two of the more important properties of ordinary linear orthogonal projections onto closed linear manifolds. In this paper, we exploit the properties of these operators t...

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Reports on the design and test of an image processing algorithm for the localization of the optic disk (OD) in low-resolution (about 20 /spl mu//pixel) color fundus images. The design relies on the combination of two procedures: 1) a Hausdorff-based template matching technique on edge map, guided by 2) a pyramidal decomposition for large scale object tracking. The two approaches are tested against...

报告了一种图像处理算法的设计和测试，用于在低分辨率（约20微米/像素）彩色眼底图像中定位视盘。该设计依赖于两种方法的结合：1）基于Hausdorff的模板匹配技术，在边缘图上进行，由2）用于大尺度目标跟踪的金字塔分解引导。这两种方法针对...

Inverse problems are essential to imaging applications. In this letter, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.

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Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

青光眼是一种导致不可逆视力丧失的慢性眼病。杯盘比（CDR）在青光眼的筛查和诊断中起着重要作用。因此，从眼底图像中准确自动地分割视盘（OD）和视杯（OC）是一项基本任务。现有的大多数方法分别分割它们，并依赖于从眼底图像中手工设计的视觉特征。在本文中，我们提出了一种深度学习架构，名为M-Net，它在单阶段多标签系统中联合解决OD和OC分割问题。所提出的M-Net主要包括多尺度输入层、U形卷积网络、侧输出层和多标签损失函数。多尺度输入层构建图像金字塔以实现多个水平的感受野尺寸。U形卷积网络被用作主体网络结构来学习丰富的层次表示，而侧输出层作为一个早期分类器，为不同尺度层产生伴随的局部预测图。最后，提出多标签损失函数以生成最终分割图。为了进一步提高分割性能，我们还引入了极坐标变换，它提供了原始图像在极坐标系中的表示。实验表明，我们的M-Net系统在ORIGA数据集上达到了最先进的OD和OC分割结果。同时，所提出的方法在ORIGA和SCES数据集上计算出的CDR值也获得了令人满意的青光眼筛查性能。

A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the re...

提出了一种在灰度断层数据集中对相邻切片进行插值的方法。利用基于B样条的非刚体配准算法建立相邻切片之间的空间对应关系，该算法优化了归一化互信息相似性度量。然后沿着由配准计算出的方向对图像强度进行线性插值...

A method for the automatic measurement of femur length in fetal ultrasound images is presented. Fetal femur length measurements are used to estimate gestational age by comparing the measurement to a typical growth chart. Using a real-time ultrasound system, sonographers currently indicate the femur endpoints on the ultrasound display station with a mouse-like device. The measurements are subjectiv...

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One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. The authors have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a may that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.

磁共振成像的主要缺点之一是缺乏对图像强度的标准和可量化解释。与其他模态（如X射线计算机断层扫描）不同，同一患者在同一扫描仪上不同时间拍摄的MR图像可能因多种扫描仪相关变化而彼此不同，因此，上述问题...

Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. (1997), this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate. List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. ...

本文利用Barrett等人（1997）近期提出的列表模式最大似然（ML）源重建理论，制定了相应的期望最大化（EM）算法，以及ML估计下噪声特性的估计方法。列表模式ML在测量空间维数阻碍测量数据分箱的情况下具有重要意义。

The new spiral multislice computed tomography (CT) scanners and the significant increase in rotation speed offer great potential for cardiac imaging with X-ray CT. The authors have therefore developed the dedicated cardiac reconstruction algorithms 180/spl deg/ multislice cardio interpolation (MCI) and 180/spl deg/ multislice cardio delta (MCD) and here offer further details and validation. The al...

新型螺旋多层计算机断层扫描（CT）扫描仪以及旋转速度的显著提高为X射线CT心脏成像提供了巨大潜力。因此，作者开发了专门的心脏重建算法180°多层心脏插值（MCI）和180°多层心脏delta（MCD），并在此提供进一步的细节和验证。该算...

This paper presents a new technique for the automatic model-based segmentation of three-dimensional (3-D) objects from volumetric image data. The development closely follows the seminal work of Taylor and Cootes on active shape models, but is based on a hierarchical parametric object description rather than a point distribution model. The segmentation system includes both the building of statistic...

本文提出了一种新的自动模型分割三维物体来自体积图像数据的技术。该发展紧密遵循Taylor和Cootes在主动形状模型方面的开创性工作，但基于分层参数化对象描述而非点分布模型。分割系统包括统计...

Guidance systems designed for neurosurgery, hip surgery, and spine surgery, and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation, and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization error (FLE) and is thus to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. This paper presents two new expressions for estimating registration accuracy of such systems and points out a danger in using a traditional measure of registration accuracy. The new expressions represent fundamental theoretical results with regard to the relationship between localization error and registration error in rigid-body, point-based registration. Rigid-body, point-based registration is achieved by finding the rigid transformation that minimizes "fiducial registration error" (FRE), which is the root mean square distance between homologous fiducials after registration. Closed form solutions have been known since 1966. The expected value (FRE/sup 2/) depends on the number N of fiducials and expected squared value of FLE, (FLE/sup 2/), but in 1979 it was shown that (FRE/sup 2/) is approximately independent of the fiducial configuration C. The importance of this surprising result seems not yet to have been appreciated by the registration community: Poor registrations caused by poor fiducial configurations may appear to be good due to a small FRE value. A more critical and direct measure of registration error is the "target registration error" (TRE), which is the distance between homologous points other than the centroids of fiducials. Efforts to characterize its behavior have been made since 1989. Published numerical simulations have shown that (TRE/sup 2/) is roughly proportional to (FLE/sup 2/)/N and, unlike (FRE/sup 2/), does depend in some way on C. Thus, FRE, which is often used as feedback to the surgeon using a point-based guidance system, is in fact an unreliable indicator of registration-accuracy. In this work the authors derive approximate expressions for (TRE/sup 2/), and for the expected squared alignment error of an individual fiducial. They validate both approximations through numerical simulations. The former expression can be used to provide reliable feedback to the surgeon during surgery and to guide the placement of markers before surgery, or at least to warn the surgeon of potentially dangerous fiducial placements; the latter expression leads to a surprising conclusion: Expected registration accuracy (TRE) is worst near the fiducials that are most closely aligned! This revelation should be of particular concern to surgeons who may at present be relying on fiducial alignment as an indicator of the accuracy of their point-based guidance systems.

设计用于神经外科、髋关节手术和脊柱手术以及其它相对刚体解剖结构的引导系统可以采用刚体变换实现图像配准。这些系统通常依赖点配准来确定变换，许多此类系统使用附着的基准标记来建立精确的配准基准点，这些点通过某种基准定位过程确定。准确性对这些系统至关重要，同样重要的是对准确性水平的了解。基于标记的系统（特别是那些标记植入骨中的系统）的一个优势是配准误差仅取决于基准定位误差（FLE），因此在很大程度上独立于被配准的特定对象。因此，应该能够根据使用模体或既往患者进行的实验测量来预测基于标记的系统的临床准确性。本文提出了两个新表达式来估计此类系统的配准准确性，并指出了使用传统配准准确性度量的危险性。新表达式代表了关于刚体点配准中定位误差与配准误差之间关系的基本理论结果。刚体点配准通过找到使“基准配准误差”（FRE）最小化的刚体变换来实现，该误差是配准后对应基准点之间的均方根距离。闭式解自1966年以来已知。期望值（FRE/sup 2/）取决于基准数量N和FLE的期望平方值（FLE/sup 2/），但在1979年发现（FRE/sup 2/）近似独立于基准配置C。这个令人惊讶的结果的重要性似乎尚未被配准学界充分认识到：由不良基准配置导致的差配准可能因为小的FRE值而显得良好。一个更关键和直接的配准误差度量是“目标配准误差”（TRE），即非基准质心的对应点之间的距离。自1989年以来一直努力描述其行为。已发表的数值模拟表明，（TRE/sup 2/）大致与（FLE/sup 2/）/N成正比，并且与（FRE/sup 2/）不同，它在某种程度上依赖于C。因此，经常用作基于点的引导系统外科医生反馈的FRE实际上是一个不可靠的配准准确性指标。在这项工作中，作者推导了（TRE/sup 2/）和单个基准的预期平方对准误差的近似表达式。他们通过数值模拟验证了这两个近似。前者表达式可用于在手术期间向外科医生提供可靠的反馈，并在术前指导标记放置，或至少警告外科医生潜在危险的基准放置；后者表达式得出一个令人惊讶的结论：预期配准准确性（TRE）在最接近对准的基准附近最差！这一发现对于目前可能依赖基准对准作为基于点的引导系统准确性指标的外科医生尤其值得关注。

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400 \thinspace000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87), and fares well on pathological cases.

人眼血管网络的状态是眼科诊断中的一个重要因素。由于血管大小不一、对比度相对较低以及可能存在微动脉瘤和出血等病理特征，眼底成像中的血管分割是一项具有挑战性的任务。过去已提出许多算法，包括无监督和有监督方法。我们提出了一种有监督分割技术，该技术使用在大量（多达400000个）样本上训练的深度神经网络，这些样本经过全局对比度归一化、零相位白化预处理，并通过几何变换和伽马校正进行数据增强。考虑了该方法的几种变体，包括结构化预测，其中网络同时分类多个像素。当应用于眼底成像的标准基准（DRIVE、STARE和CHASE数据库）时，该网络在ROC曲线下面积（高达>0.99）和分类准确性（高达>0.97）上显著优于先前的算法。该方法还对中心血管反射现象具有鲁棒性，对细小血管检测敏感（灵敏度>0.87），并且在病理情况下表现良好。

Artifacts can result when reconstructing a dynamic image sequence from inconsistent, as well as insufficient and truncated, cone beam single photon emission computed tomography (SPECT) projection data acquired by a slowly rotating gantry. The artifacts can lead to biases in kinetic model parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. Howev...

当从由慢速旋转机架采集的不一致、不充分且截断的锥束单光子发射计算机断层扫描（SPECT）投影数据重建动态图像序列时，可能会产生伪影。这些伪影可能导致通过将感兴趣区域叠加在图像上生成的时间-活动曲线估计的动力学模型参数出现偏差。

A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.

提出了一种基于多配准组合的新型图谱分割方法。将多个图谱配准到目标图像。为了得到目标的分割，将图谱图像的标签传播到目标图像。通过空间变化的决策融合权重组合传播的标签，这些权重来自对配准成功的局部评估。此外，提出了一种与统计模式识别理论中的序贯前向选择等效的图谱选择过程。将该方法与三种现有的基于图谱的分割方法进行比较：1）单图谱分割，2）平均形状图谱分割，3）平均决策融合的多图谱分割。这些方法在胸部CT扫描中的心脏和主动脉分割上进行了测试。结果表明，所提出的方法优于其他方法，结果与独立人类观察者非常接近。此外，额外的图谱选择步骤在相当的性能下实现了更快的分割。

Describes a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. The authors have validated the technique on simulated as well as on real MR images of the brain.

描述了一种完全自动化的方法，用于对脑部磁共振（MR）图像进行基于模型的组织分类。该方法将分类与模型参数估计交错进行，每次迭代均改进分类。该算法能够分割单谱和多谱MR图像，校正MR信号不均匀性，并利用马尔可夫随机场（MRF）纳入上下文信息。使用包含组织类别空间位置先验知识的数字脑图谱来初始化算法。这使得该方法完全自动化，从而提供客观且可重复的分割结果。作者已在模拟和真实脑部MR图像上验证了该技术。

The maximum likelihood (ML) approach to estimating the radioactive distribution in the body cross section has become very popular among researchers in emission computed tomography (ECT) since it has been shown to provide very good images compared to those produced with the conventional filtered backprojection (FBP) algorithm. The expectation maximization (EM) algorithm is an often-used iterative a...

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Gastrointestinal (GI) tract examination with spiral/helical computed tomography (CT) is currently performed by slice-based inspection of axial images. CT colography is a recent advance which allows an intraluminal visualization of the colon, similar to endoscopy. Various rendering algorithms have been developed with promising results, however navigation through the complex, tortuous anatomy of the...

目前，螺旋/螺旋计算机断层扫描（CT）检查胃肠道是通过逐层检查轴向图像进行的。CT结肠成像是一项最新进展，它允许像内窥镜检查一样对结肠进行腔内可视化。已经开发了各种渲染算法，并取得了有希望的结果，然而在复杂、曲折的解剖结构中导航……

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation).

肺X射线计算机断层扫描(CT)图像的分割是大多数肺部图像分析应用的前置步骤。本文提出了一种全自动方法，用于在三维（3D）肺部X射线CT图像中识别肺。该方法包括三个主要步骤。首先，通过灰度阈值从CT图像中提取肺区域。然后，通过动态规划识别前后连接处，将左肺和右肺分离。最后，使用一系列形态学操作沿着纵隔平滑不规则边界，以获得与手动分析一致的结果，其中只有最中心的肺动脉被排除在肺区域之外。该方法通过处理来自八名正常受试者的三维CT数据集进行了测试，每名受试者每隔两周以90%肺活量成像三次。作者通过将其自动方法与两位图像分析师手动追踪的边界进行比较来展示结果。在所有体积上平均，计算机与人工分析之间的均方根差为0.8像素（0.54毫米）。三次扫描中组织含量的平均受试者内变化为2.75%±2.29%（均值±标准差）。

Examines the use of the algebraic reconstruction technique (ART) and related techniques to reconstruct 3-D objects from a relatively sparse set of cone-beam projections. Although ART has been widely used for cone-beam reconstruction of high-contrast objects, e.g., in computed angiography, the work presented here explores the more challenging low-contrast case which represents a little-investigated...

研究了使用代数重建技术（ART）及相关技术从相对稀疏的锥束投影中重建三维物体。尽管ART已广泛应用于高对比度物体的锥束重建，例如在计算机断层血管造影中，但本文探讨了更具挑战性的低对比度情况，这是一个研究较少的领域。

Following B.B. Mandelbrot's fractal theory (1982), it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion is discussed. Two applications are found: (1) classification; (2) edge enhancement and detection. For the pur...

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Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using ...

混合模型常用于图像的统计分割。例如，它们可用于将结构医学图像分割成不同的物质类型，或将统计参数图分割为功能成像中的激活和非激活脑区域。空间混合模型已被开发出来，通过空间正则化增强直方图信息，使用...

The authors consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. T...

作者考虑基于一组对应的解剖点标志点和近似薄板样条的弹性图像配准。该方法是原始插值薄板样条方法的扩展，允许考虑标志点定位误差。这一扩展对于临床应用很重要，因为标志点提取总是容易出错。

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

本文提出了一种新的监督方法用于数字视网膜图像中的血管检测。该方法采用神经网络方案进行像素分类，并计算由灰度级和基于矩不变特征组成的7维向量用于像素表示。该方法在公开的DRIVE和STARE数据库上进行了评估，这两个数据库广泛用于此目的，因为它们包含由专家精确标记血管结构的视网膜图像。该方法在两个测试图像集上的性能优于文献中其他现有解决方案。该方法在STARE图像中的血管检测尤为准确。将其应用于该数据库（即使神经网络是在DRIVE数据库上训练的）也优于所有分析的分割方法。其在不同图像条件下的有效性和鲁棒性，以及简单性和快速实现，使得该血管分割方案适用于视网膜图像计算机分析，如早期糖尿病视网膜病变的自动筛查。

A new class of acquisition schemes for helical cone-beam computed tomography (CB-CT) scanning is introduced, and their effect on the reconstruction methods is analyzed. These acquisition schemes are based on a new detector shape that is bounded by the helix. It will be shown that the data acquired with these schemes are compatible with exact reconstruction methods, and the adaptation of exact reco...

介绍了一类用于螺旋锥束计算机断层扫描（CB-CT）的新型采集方案，并分析了它们对重建方法的影响。这些采集方案基于一种由螺旋线界定的新型探测器形状。将证明这些方案采集的数据与精确重建方法兼容，并且精确重建方法的适应...

Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus segmentation is a prerequisite for various quantitative analyses including automatic morphological feature computation. However, it remains to be a c...

计算机辅助的组织病理学标本图像分析可能为早期检测和改善诸如脑肿瘤、胰腺神经内分泌肿瘤（NET）和乳腺癌等疾病的特征描述提供支持。自动细胞核分割是包括自动形态特征计算在内的各种定量分析的前提。然而，它仍然是一个...