Most Cited Articles

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional neural network (CNN)-based segmentation methods with the goal of reducing HD directly. We propose three methods to estimate HD from the segmentation probability map produced by a CNN. One method makes use of the distance transform of the segmentation boundary. Another method is based on applying morphological erosion on the difference between the true and estimated segmentation maps. The third method works by applying circular/spherical convolution kernels of different radii on the segmentation probability maps. Based on these three methods for estimating HD, we suggest three loss functions that can be used for training to reduce HD. We use these loss functions to train CNNs for segmentation of the prostate, liver, and pancreas in ultrasound, magnetic resonance, and computed tomography images and compare the results with commonly-used loss functions. Our results show that the proposed loss functions can lead to approximately 18-45% reduction in HD without degrading other segmentation performance criteria such as the Dice similarity coefficient. The proposed loss functions can be used for training medical image segmentation methods in order to reduce the large segmentation errors.

中文摘要翻译待生成

This paper presents a quantitative comparison of three types of information available for 52 patients scheduled for carotid endarterectomy: subjective classification of the ultrasound images obtained during scanning before operation, first- and second-order statistical features extracted from regions of the plaque in still ultrasound images from three orthogonal scan planes and finally a histologi...

本文对52名计划进行颈动脉内膜切除术患者的三种可用信息进行了定量比较：术前扫描过程中获得的超声图像的主观分类、从三个正交扫描平面的静止超声图像中的斑块区域提取的一阶和二阶统计特征，最后是组织学检查。

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network’s novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network’s performance on different subpopulations of the screening population, the model’s design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier .

中文摘要翻译待生成

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

提出了一种解决多模态医学图像配准问题的新方法，该方法使用信息论中的基本概念——互信息（MI）或相对熵作为新的匹配准则。本文提出的方法应用互信息来测量两幅图像中对应体素图像强度之间的统计依赖性或信息冗余，并假设当图像几何对齐时互信息最大。互信息最大化是一个非常通用且强大的准则，因为未对该依赖关系的性质做任何假设，也未对所涉及模态的图像内容施加限制性约束。通过与立体定向配准解决方案的比较，验证了互信息准则在计算机断层扫描（CT）、磁共振（MR）和光子发射断层扫描（PET）图像刚体配准中的准确性，同时评估了其对于实现问题（如插值和优化）和图像内容（包括部分重叠和图像退化）的鲁棒性。结果表明，可以完全自动地实现相对于立体定向参考解的亚体素精度，且无需任何预先分割、特征提取或其他预处理步骤，这使得该方法非常适合临床应用。

Using statistical methods the reconstruction of positron emission tomography (PET) images can be improved by high-resolution anatomical information obtained from magnetic resonance (MR) images. The authors implemented two approaches that utilize MR data for PET reconstruction. The anatomical MR information is modeled as a priori distribution of the PET image and combined with the distribution of t...

利用统计方法，正电子发射断层成像（PET）图像的重建可以通过从磁共振（MR）图像中获得的高分辨率解剖信息得到改善。作者实现了两种利用MR数据进行PET重建的方法。解剖MR信息被建模为PET图像的先验分布，并与...分布相结合。

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning. ResViT’s generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics.

中文摘要翻译待生成

The accuracy of image-guided neurosurgery generally suffers from brain deformations due to intraoperative changes. These deformations cause significant changes of the anatomical geometry (organ shape and spatial interorgan relations), thus making intraoperative navigation based on preoperative images error prone. In order to improve the navigation accuracy, the authors developed a biomechanical mo...

图像引导神经外科的准确性通常受到术中变化导致的脑变形的影响。这些变形会引起解剖几何（器官形状和空间器官间关系）的显著变化，从而使基于术前图像的术中导航容易出错。为了提高导航精度，作者开发了一种生物力学模型...

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

提出了一种用于视网膜二维彩色图像中血管自动分割的方法。该方法可用于视网膜图像的计算机分析，例如糖尿病视网膜病变的自动筛查。该系统基于提取图像脊线，这些脊线与血管中心线大致重合。脊线用于组成线元素形式的基元...

A method is described for the automated detection of microcalcifications in digitized mammograms. The method is based on the Laplacian scale-space representation of the mammogram only. First, possible locations of microcalcifications are identified as local maxima in the filtered image on a range of scales. For each finding, the size and local contrast is estimated, based on the Laplacian response...

描述了一种用于数字化乳腺X线摄影中微钙化自动检测的方法。该方法仅基于乳腺X线摄影的拉普拉斯尺度空间表示。首先，在一系列尺度上，将微钙化的可能位置识别为滤波后图像中的局部最大值。对于每个发现，根据拉普拉斯响应估计其大小和局部对比度...

The authors developed a simple method for determining the presampling modulation transfer function (MTF). which includes the unsharpness of the detector and the effect of the sampling aperture, in digital radiographic (DR) systems. With this method, the presampling MTF is determined by the Fourier transform of a 'finely sampled' line spread function (LSF) obtained with a slightly angulated slit in a single exposure. Since the effective sampling distance becomes much smaller than the original sampling distance of the DR system, the effect of aliasing on the MTF calculations can be eliminated. The authors applied this method to the measurement of the presampling MTF of a compound radiographic system and examined the directional dependence, the effect of exponential extrapolation, and the effect of different sampling distances. It is shown that the technique of multiple slit exposure and exponential extrapolation of the LSF tail, which has been commonly used in analog seven-film systems, can be employed in DR systems. The authors determined the glare fraction in order to estimate the component of low-frequency drop mainly due to 'glare'. >

中文摘要翻译待生成

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained. >

作者定义了用于从投影数据恢复图像的标准算法（如期望最大化，EM）的有序子集处理。有序子集方法将投影数据分组为有序的子集序列（或块）。有序子集EM的一次迭代定义为一次通过所有子集，在每个子集中使用当前估计来初始化应用

Transformer-based methods are recently popular in vision tasks because of their capability to model global dependencies alone. However, it limits the performance of networks due to the lack of modeling local context and global-local correlations of multi-scale features. In this paper, we present MISSFormer, a Medical Image Segmentation tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two appealing designs: 1) a feed-forward network in transformer block of U-shaped encoder-decoder structure is redesigned, ReMix-FFN, which explore global dependencies and local context for better feature discrimination by re-integrating the local context and global dependencies; 2) a ReMixed Transformer Context Bridge is proposed to extract the correlations of global dependencies and local context in multi-scale features generated by our hierarchical transformer encoder. The MISSFormer shows a solid capacity to capture more discriminative dependencies and context in medical image segmentation. The experiments on multi-organ, cardiac segmentation and retinal vessel segmentation tasks demonstrate the superiority, effectiveness and robustness of our MISSFormer. Specifically, the experimental results of MISSFormer trained from scratch even outperform state-of-the-art methods pre-trained on ImageNet, and the core designs can be generalized to other visual segmentation tasks. The code has been released on Github: https://github.com/ZhifangDeng/MISSFormer .

中文摘要翻译待生成

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

从头训练深度卷积神经网络（CNN）是困难的，因为它需要大量标记的训练数据和丰富的专业知识来确保正确收敛。一个有前景的替代方案是微调一个已经使用大量标记自然图像预训练的CNN。然而，自然图像与医学图像之间的显著差异...

The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRIs) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout...

探索使用小波变换来检测在两种不同实验条件下获取的脑功能磁共振图像（fMRIs）之间的差异。该方法得益于这样一个事实：平滑且空间局部化的信号可以用少量局部小波系数表示，而白噪声的功率均匀分布在整个...

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

我们提出了一种用于功能磁共振成像（fMRI）数据的概率独立成分分析（ICA）集成方法，该方法允许在高斯噪声存在下进行非方形混合。为了避免过拟合，我们通过对数据真实维度的贝叶斯分析（即激活和非高斯噪声源的数量）来客观估计高斯噪声的量。这使我们能够进行概率建模，并实现数据的渐近唯一分解。它减少了解释问题，因为每个最终的独立成分现在更可能仅由一个物理或生理过程引起。我们还描述了标准ICA的其他改进，例如时间预白化和时间序列的方差归一化，后者在存在弱激活时的降维背景下特别有用。我们讨论了关于源过程时空性质的先验信息的使用，以及使用高斯混合模型进行推理的备择假设检验方法。我们通过在真实和人工fMRI数据上展示和评估我们方法的性能，并与经典ICA和GLM分析得到的时空准确性进行比较。

Presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, the authors consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classe...

提出了一种全自动的三维脑组织分类方法用于磁共振（MR）图像。由于部分体积效应，MR图像体素可能由多种组织类型混合而成。因此，作者认为在脑数据集中不仅存在三种主要脑组织类型：灰质、白质和脑脊液，称为纯类...

The solution of impedance distribution in electrical impedance tomography is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods have been popular in the solution of many inverse problems. The regularization matrices that are usually used with the Tikhonov method are more or less ad hoc and the implicit prior assumptions are, thus, in many cases inappropriate. In this paper, the authors propose an approach to the construction of the regularization matrix that conforms to the prior assumptions on the impedance distribution. The approach is based on the construction of an approximating subspace for the expected impedance distributions. It is shown by simulations that the reconstructions obtained with the proposed method are better than with two other schemes of the same type when the prior is compatible with the true object. On the other hand, when the prior is incompatible with the true object, the method will still give reasonable estimates.

电阻抗成像中阻抗分布的求解是一个非线性逆问题，需要使用正则化方法。广义Tikhonov正则化方法在许多逆问题的求解中很受欢迎。通常与Tikhonov方法一起使用的正则化矩阵或多或少是特设的，因此隐含的先验假设在许多情况下是不合适的。在本文中，作者提出了一种构建与阻抗分布先验假设相一致的正则化矩阵的方法。该方法基于为预期的阻抗分布构建近似子空间。模拟结果表明，当先验与真实对象一致时，所提出的方法获得的重建结果优于其他两种同类方案。另一方面，当先验与真实对象不一致时，该方法仍能给出合理的估计。

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For m...

中文摘要翻译待生成

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 $\times$ 3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

在脑肿瘤中，胶质瘤是最常见且最具侵袭性的，其最高级别会导致预期寿命极短。因此，治疗计划是改善肿瘤患者生活质量的关键阶段。磁共振成像（MRI）是一种广泛用于评估这些肿瘤的成像技术，但MRI产生的大量数据使得手动分割在合理时间内难以完成。

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step ...

中文摘要翻译待生成

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

从心脏磁共振图像（多层二维电影MRI）中勾画左心室腔、心肌和右心室是建立诊断的常见临床任务。因此，过去几十年中，相应任务的自动化一直是深入研究的主题。本文介绍了“自动心脏诊断挑战”数据集（ACDC），这是用于心脏MRI（CMR）评估的最大公开可用且完全标注的数据集。该数据集包含来自150个多设备CMRI记录的数据，并附有两位医学专家的参考测量和分类。本文的首要目标是衡量最先进的深度学习方法在评估CMRI方面能达到何种程度，即分割心肌和两个心室以及分类病理。继2017年MICCAI-ACDC挑战赛之后，我们报告了九个研究小组提供的分割任务和四个小组提供的分类任务的深度学习方法的结果。结果表明，最佳方法忠实地再现了专家分析，自动提取临床指标的平均相关系数为0.97，自动诊断的准确率为0.96。这些结果显然为高精度全自动心脏CMRI分析打开了大门。我们还确定了深度学习方法仍会失败的场景。该数据集和详细结果均可在线公开获取，平台将保持开放以便新提交。

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.

人类皮层卷积的高度复杂性对工程师测量和比较它，以及对生物学家和医生理解它都极具挑战性。在本文中，我们提出了一种基于表面的皮层回旋量化方法。我们的方法使用精确的三维皮层重建，并在整个皮层表面的数千个点上计算回旋的局部测量。通过对一组患有22q11缺失综合征的儿童与对照个体的临床研究，展示了我们的方法在精确定位和识别脑回异常方面的潜力。

In a previous study (J.B. West et al., J. Comput. Assist. Tomogr., vol. 21, p. 554-66, 1997) the authors demonstrated that automatic retrospective registration algorithms can frequently register magnetic resonance (MR) and computed tomography (CT) images of the brain with an accuracy of better than 2 mm, but in that same study the authors found that such algorithms sometimes fail, leading to error...

在之前的一项研究（J.B. West等人，《计算机辅助断层扫描杂志》，第21卷，第554-66页，1997年）中，作者证明了自动回顾性配准算法通常能够以优于2毫米的精度配准脑部磁共振（MR）和计算机断层扫描（CT）图像，但在同一研究中，作者发现此类算法有时会失败，导致误差...

Preliminary evaluation of recently developed large-area flat panel detectors (FPDs) indicates that FPDs have some potential advantages: compactness, absence of geometric distortion and veiling glare with the benefits of high resolution, high detective quantum efficiency (DQE), high frame rate and high dynamic range, small image lag (<1%), and excellent linearity (/spl sim/1%). The advantages of the new FPD make it a promising candidate for cone-beam volume computed tomography (CT) angiography (CBVCTA) imaging. The purpose of this study is to characterize a prototype FPD-based imaging system for CBVCTA applications. A prototype FPD-based CBVCTA imaging system has been designed and constructed around a modified GE 8800 CT scanner. This system is evaluated for a CBVCTA imaging task in the head and neck using four phantoms and a frozen rat. The system is first characterized in terms of linearity and dynamic range of the detector. Then, the optimal selection of kVps for CBVCTA is determined and the effect of image lag and scatter on the image quality of the CBVCTA system is evaluated. Next, low-contrast resolution and high-contrast spatial resolution are measured. Finally, the example reconstruction images of a frozen rat are presented. The results indicate that the FPD-based CBVCT can achieve 2.75-1p/mm spatial resolution at 0% modulation transfer function (MTF) and provide more than enough low-contrast resolution for intravenous CBVCTA imaging in the head and neck with clinically acceptable entrance exposure level. The results also suggest that to use an FPD for large cone-angle applications, such as body angiography, further investigations are required.

最近开发的大面积平板探测器（FPD）的初步评估表明，FPD具有一些潜在优势：紧凑性、无几何失真和光晕，同时具有高分辨率、高探测量子效率（DQE）、高帧率、高动态范围、小图像滞后（<1%）和优异的线性度（/spl sim/1%）。新型FPD的这些优势使其成为锥束容积计算机断层扫描（CT）血管成像（CBVCTA）的有前景候选。本研究旨在表征一种用于CBVCTA应用的原型FPD成像系统。围绕改进的GE 8800 CT扫描仪设计和构建了一种基于FPD的原型CBVCTA成像系统。使用四个体模和一只冷冻大鼠对该系统在头颈部CBVCTA成像任务中进行评估。首先根据探测器的线性度和动态范围进行表征。然后确定CBVCTA的最佳千伏峰值选择，并评估图像滞后和散射对CBVCTA系统图像质量的影响。接下来测量低对比度分辨率和高对比度空间分辨率。最后展示冷冻大鼠的重建图像示例。结果表明，基于FPD的CBVCT可以在0%调制传递函数（MTF）下达到2.75线对/毫米的空间分辨率，并在临床可接受的入射暴露水平下为头颈部静脉内CBVCTA成像提供足够高的低对比度分辨率。结果还提示，将FPD用于大锥角应用（如体部血管成像）需要进一步研究。

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

中文摘要翻译待生成