Volume 38, Issue 5

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet , a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction , which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available.

最近，密集连接在计算机视觉中引起了广泛关注，因为它们促进了训练期间的梯度流动和隐式深度监督。特别是，DenseNet以前馈方式将每一层连接到其他所有层，并在自然图像分类任务中表现出令人印象深刻的性能。我们提出了HyperDenseNet，一种3D全卷积神经网络，将密集连接的定义扩展到多模态分割问题。每个成像模态都有一个路径，密集连接不仅发生在同一路径内的层对之间，还发生在不同路径的层对之间。这与现有的多模态CNN方法形成对比，这些方法完全依赖单个联合层（或抽象级别）来融合多个模态，通常是在网络的输入或输出处。因此，提出的网络有完全的自由度来学习模态之间更复杂的组合，在所有抽象级别内部和之间，这显著增加了学习表示。我们在两个不同且高度竞争的多模态脑组织分割挑战中报告了广泛的评估，即iSEG 2017和MRBrainS 2013，前者关注六个月婴儿数据，后者关注成人图像。HyperDenseNet在许多最先进的分割网络上取得了显著改进，在两个基准测试中均排名第一。我们还提供了特征重用的全面实验分析，证实了超密集连接在多模态表示学习中的重要性。我们的代码已公开可用。

Deformable image registration can be time consuming and often needs extensive parameterization to perform well on a specific application. We present a deformable registration method based on a 3-D convolutional neural network, together with a framework for training such a network. The network directly learns transformations between pairs of 3-D images. The network is trained on synthetic random tr...

变形图像配准可能耗时，并且通常需要大量参数化才能在特定应用上表现良好。我们提出了一种基于三维卷积神经网络的变形配准方法，以及训练此类网络的框架。该网络直接学习三维图像对之间的变换。该网络在合成随机变换上训练……

Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these me...

组织病理学检查是当今癌症诊断的金标准。然而，这项任务耗时且容易出错，因为它需要病理学家进行详细的视觉检查和解读。数字病理学旨在通过提供定量分析数字化组织病理组织图像的计算机化方法来缓解这些问题。这些方法的性能...

Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions. We propose synthesizing pathology in medical images as a means to overcome these challenges. We implement a deep convolutional generative adversarial...

医学数据集通常高度不平衡，常见病症过度代表，而罕见疾病代表不足。由于隐私问题，医疗保健机构之间难以汇总大型数据集。我们提出在医学图像中合成病理作为克服这些挑战的一种方法。我们实现了一个深度卷积生成对抗网络...

Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints-first, they require resizing the volume to the lower-resolutional refer...

三维扫描中的分割在当前临床实践中发挥着越来越重要的作用，支持诊断、组织量化或治疗计划。当前基于卷积神经网络的三维方法通常至少受到由实现约束引起的三个主要问题的影响——首先，它们需要将体积调整为较低分辨率的参考...

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this proc...

心血管磁共振（CMR）扫描的有效性取决于操作者根据扫描对象正确调整采集参数的能力以及可能出现的成像伪影（如心脏和呼吸运动）。在临床实践中，通过视觉评估采集的图像进行质量控制步骤；然而，这一过程...

Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer’s disease (AD). Brain connectivity network inferred from the functional magnetic resonance imaging data has been prevalently used to identify the individuals with MCI/AD from the normal controls. The capability to detec...

轻度认知障碍（MCI）检测很重要，可以采取适当的干预措施来延缓或防止其发展为严重阶段，包括阿尔茨海默病（AD）。从功能磁共振成像数据推断出的脑连接网络已被普遍用于从正常对照中识别MCI/AD个体。检测能力...

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to: 1) large anatomical and spatial variations of splenomegaly; 2) large...

脾肿大是脾脏异常增大的表现，是肝脾疾病的非侵入性临床生物标志物。自动分割方法对于从临床获取的腹部磁共振成像（MRI）扫描中有效量化脾肿大至关重要。然而，该任务面临挑战：1）脾肿大具有较大的解剖和空间变异；2）...

Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of different cell types (e.g., epithelial cells) and sub-cellular components (e.g., nuclei), provided that we can accurately classify the pixels belongi...

中红外波段的高光谱成像（HSI）提供了亚细胞空间分辨率下的光谱化学和组织结构信息。通过分析不同细胞类型（如上皮细胞）和亚细胞成分（如细胞核）的中红外光谱，可以直接评估疾病状态，前提是我们能够准确分类属于...的像素。

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this paper investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. Seventy ...

病理学中的众包已在假定非专家可完成的任务上实施。对数字显微图像中更复杂元素（如解剖结构）的标注需求仍然很高。因此，本文研究了实现高层次图像对象众包标注的条件，这是一项被认为需要专家知识的复杂任务。七十...

Quasi-static elasticity imaging techniques rely on model-based mathematical inverse methods to estimate mechanical parameters from force–displacement measurements. These techniques introduce simplifying assumptions that preclude exploration of unknown mechanical properties with potential diagnostic value. We previously reported a data-driven approach to elasticity imaging using artificial neural n...

准静态弹性成像技术依赖于基于模型的数学逆方法，从力-位移测量中估计力学参数。这些技术引入了简化假设，阻碍了对具有潜在诊断价值的未知力学特性的探索。我们之前报道了一种使用人工神经网络的数据驱动弹性成像方法。

Building a data-driven model to localize the origin of ventricular activation from 12-lead electrocardiograms (ECG) requires addressing the challenge of large anatomical and physiological variations across individuals. The alternative of a patient-specific model is, however, difficult to implement in clinical practice because the training data must be obtained through invasive procedures. In this ...

建立一个数据驱动模型，从12导联心电图中定位心室激动的起源，需要应对个体间巨大的解剖和生理变异挑战。然而，患者特异性模型的替代方案在临床实践中难以实施，因为训练数据必须通过侵入性程序获得。在这……

Dual-VENC strategies have been proposed to improve the velocity-to-noise ratio in phase-contrast MRI. However, they are based on aliasing-free high-VENC data. The aim of this paper is to propose a dual-VENC velocity estimation method allowing high-VENC aliased data. For this purpose, we reformulate the phase-contrast velocity as a least squares estimator, providing a natural framework for includin...

双VENC策略已被提出以提高相位对比MRI中的速度噪声比。然而，它们基于无混叠的高VENC数据。本文旨在提出一种允许高VENC混叠数据的双VENC速度估计方法。为此，我们将相位对比速度重新表示为最小二乘估计量，为包括...提供了一个自然的框架。

Hyperpolarized (HP) gas (e.g., 3He or 129Xe) dynamic MRI could visualize the lung ventilation process, which provides characteristics regarding lung physiology and pathophysiology. Compressed sensing (CS) is generally used to increase the temporal resolution of such dynamic MRI. Nevertheless, the acceleration factor of CS is constant, which results in difficulties in precisely observing and/or mea...

超极化（HP）气体（如³He或¹²⁹Xe）动态MRI能够可视化肺部通气过程，从而提供有关肺部生理和病理生理学特征的信息。压缩感知（CS）通常用于提高此类动态MRI的时间分辨率。然而，CS的加速因子是恒定的，这导致在精确观察和/或测量方面存在困难…

Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of r...

现有的单光子发射计算机断层扫描（SPECT）重建方法大多基于离散模型，这些模型可视为连续数据采集过程的分段常数近似。由于分段常数近似的精度阶数较低，传统离散模型引入了不可约的模型误差，这是重建质量提升的瓶颈。

While an accurate image reconstruction of digital breast tomosynthesis (DBT) is fundamentally impossible due to its limited data, the DBT is increasingly used in clinics for its rich image information at a relatively low dose. One of the dominant image artifacts in DBT that hinders a faithful diagnosis is high-density object artifact in conjunction with a limited angle problem. In this paper, we d...

虽然由于数据有限，数字乳腺断层合成（DBT）的精确图像重建基本上是不可能的，但DBT因其相对低剂量下的丰富图像信息而越来越多地在临床中使用。妨碍准确诊断的主要图像伪影之一是与有限角度问题相关的高密度物体伪影。在本文中，我们……

The cerebral vascular system provides a means to meet the constant metabolic needs of neuronal activities in the brain. Within the cerebral capillary bed, the interactions of spatial and temporal hemodynamics play a deterministic role in oxygen diffusion, however, the progression of which remains unclear. Taking the advantages of high-spatiotemporal resolution of optical coherence tomography capil...

脑血管系统为满足大脑神经元活动的持续代谢需求提供了途径。在脑毛细血管床中，时空血流动力学的相互作用在氧气扩散中起决定性作用，然而其发展过程仍不明确。利用光学相干断层扫描高时空分辨率的优势，毛细...

The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series...

动态PET中心肌血流量（MBF）的估计可能受到许多不同过程的影响。特别是在临床应用中，一个主要的误差来源是患者运动。患者运动或宏观运动会在不同的PET帧之间以及PET帧与CT衍生的衰减图之间产生位移，导致基于体素时间序列的MBF计算出现误差。

It is widely accepted that optimization of imaging system performance should be guided by task-based measures of image quality. It has been advocated that imaging hardware or data-acquisition designs should be optimized by use of an ideal observer that exploits full statistical knowledge of the measurement noise and class of objects to be imaged, without consideration of the reconstruction method....

广泛认可，成像系统性能的优化应基于任务的图像质量度量。有观点认为，成像硬件或数据采集设计应使用理想观察者进行优化，该观察者利用测量噪声和待成像物体类别的完整统计知识，而不考虑重建方法。

Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer s...

显微镜因其高分辨率和能够对多种不同生物标志物进行染色而被广泛应用于大脑研究。由于整个大脑通常被切片以进行组织染色和成像，从这些切片重建三维大脑体积对于可视化和分析至关重要。最近发展的组织透明技术和先进的共聚焦显微镜使得多层...

Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technica...

广告，IEEE。IEEE Collabratec是一个新的集成在线社区，IEEE会员、研究人员、作者和技术专业人士可以在其中与相似领域兴趣的人进行网络交流和协作，以及创建和管理内容。具有一套强大的在线网络和协作工具，IEEE Collabratec允许您根据地理位置、技术...进行连接。

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

请有意投稿的作者提交新的、未发表的稿件，以便纳入本次征文通知所述的即将举行的活动。

Presents the table of contents for this issue of the publication.

介绍本期出版物的目录。

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

介绍本期出版物的编辑委员会、理事会、现任工作人员、委员会成员和/或学会编辑名单。

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

这些说明为本出版物准备论文提供了指导。为在本期刊发表论文的作者提供信息。