Volume 38, Issue 9

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer’s ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

从二维超声心动图像中勾画心脏结构是临床诊断中常见任务。过去几十年，该任务的自动化一直是研究热点。本文评估了最先进的编码器-解码器深度卷积神经网络方法在二维超声心动图像评估上的表现，即分割心脏结构和估计临床指标，使用一个专门为此目标设计的数据集。因此，我们引入了心脏多结构超声分割数据集，这是目前最大且完全标注的公开数据集，用于超声心动评估。该数据集包含来自500名患者的双腔和四腔采集，由一位心脏病专家对整个数据集进行参考测量，并由三位心脏病专家对50名患者的子集进行测量。结果表明，基于编码器-解码器的架构优于最先进的非深度学习方法，并忠实再现了专家对左心室舒张末期和收缩末期容积的分析，平均相关性达0.95，绝对平均误差为9.5毫升。关于左心室的射血分数，结果较为对比，平均相关系数为0.80，绝对平均误差为5.6%。尽管这些结果低于观察者间评分，但略优于观察者内评分。基于这一观察，定义了改进方向，为二维超声心动图像的准确和全自动分析打开了大门。

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-cl...

在皮肤镜图像中自动进行皮肤病变分类是提高诊断性能和减少黑色素瘤死亡的重要方法。尽管深度卷积神经网络（DCNN）在许多图像分类任务中取得了突破性进展，但由于训练数据不足、类间相似性和类内差异等原因，皮肤病变的准确分类仍然具有挑战性。

Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifier. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial ...

近期研究表明，生成对抗网络（GANs）可成功应用于图像合成和半监督学习，在给定小规模标注数据库和大规模未标注数据库的情况下，目标是训练一个强大的分类器。本文中，我们训练了一个视网膜图像合成器和一个半监督学习方法，用于利用对抗式...进行自动青光眼评估。

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a sha...

深度学习方法在心脏磁共振（CMR）图像分割中取得了最先进的性能。然而，大多数方法侧重于学习图像强度特征进行分割，而解剖学形状先验的整合较少受到关注。在本文中，我们将多任务深度学习方法与图谱传播相结合，开发了一种形状精化的分割方法。

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, to generate normal or pathological deformations for any new image, or to transport deformations from one image pair t...

我们提出从数据中学习一个低维概率变形模型，该模型可用于变形配准和分析。潜在变量模型将相似的变形映射到编码空间中的邻近位置。它能够比较变形，为任何新图像生成正常或病理变形，或者将变形从一个图像对传输到另一个图像对...

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6–9 months of age), due to inherent myelination and maturation process, WM and...

将婴儿大脑磁共振（MR）图像精确分割为白质（WM）、灰质（GM）和脑脊液是早期研究神经发育障碍中大脑生长模式和形态变化不可或缺的基础。然而，在等信号期（约6-9个月大），由于固有的髓鞘形成和成熟过程，WM和...

Ultrasound localization microscopy can map blood vessels with a resolution much smaller than the wavelength by localizing microbubbles. The current implementations of the technique are limited to 2-D planes or small fields of view in 3-D. These suffer from minutelong acquisitions, out-of-plane microbubbles, and tissue motion. In this paper, we exploit the recent development of 4D ultrafast ultraso...

超声定位显微成像可以通过定位微泡来绘制分辨率远小于波长的血管图。目前该技术的实现仅限于二维平面或小视野的三维成像，存在采集时间长、微泡脱出平面和组织运动等问题。本文利用最近发展的四维超快超声技术，实现了...

Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet the demands of the increasing interest in quantification of CAC, i.e., coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. The current automatic calcium scoring met...

心血管疾病（CVD）是全球主要的死亡原因。冠状动脉钙化（CAC）的量是CVD事件的强风险因素。为了满足对CAC量化（即冠状动脉钙化评分）日益增长的需求，特别是作为筛查和研究中非请求性发现，已提出自动方法。目前的自动钙化评分方法...

Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to the EIT head imaging. This paper studies the application of capacitively coupled electrical impedance tomography (CCEIT) ...

电阻抗断层成像（EIT）因其紧凑性及在床旁和紧急情况下的潜在应用，被认为是脑卒中成像的候选技术。电极-皮肤接触阻抗和颅骨的低电导率给EIT头部成像带来了一些实际挑战。本文研究了电容耦合电阻抗断层成像（CCEIT）的应用...

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train convolutional neural networks (CNNs) with a training set...

细胞核检测是一个具有挑战性的研究课题，因为细胞图像质量的限制和核形态的多样性，即不同的核形状、大小以及多个细胞核之间的重叠。这一直是一个长期关注的话题，深度学习方法最近取得了有希望的成果。这些方法使用训练集训练卷积神经网络（CNN）...

In magnetic resonance imaging (MRI), different imaging settings lead to various intensity distributions for a specific imaging object, which brings huge diversity to data-driven medical applications. To standardize the intensity distribution of magnetic resonance (MR) images from multiple centers and multiple machines using one model, a cycle generative adversarial network (CycleGAN)-based framewo...

在磁共振成像（MRI）中，不同的成像设置会导致特定成像对象的强度分布不同，这给数据驱动的医学应用带来了巨大的多样性。为了使用一个模型标准化来自多个中心和多种机器的磁共振（MR）图像的强度分布，提出了一种基于循环生成对抗网络（CycleGAN）的框架……

In this paper, we are proposing a novel motion correction algorithm for high-resolution OR-PAM imaging. Our algorithm combines a modified demons-based tracking approach with a newly developed multi-scale vascular feature matching method to track motion between adjacent B-scan images without needing any reference object. We first applied this algorithm to correct motion artifacts within one three-d...

本文提出了一种用于高分辨率光学分辨率光声显微镜（OR-PAM）成像的新型运动校正算法。该算法结合了改进的基于Demons的跟踪方法和新开发的多尺度血管特征匹配方法，无需任何参考对象即可跟踪相邻B扫描图像之间的运动。我们首先将该算法应用于校正三维...中的运动伪影。

A handheld approach to 3D photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm × 64 mm × 40 mm. Besides,...

手持式三维光声成像方法在临床应用中至关重要。为此，我们开发了一种三维手持式光声成像仪，用于动态（时间和空间）体积可视化。在该三维成像仪中，光学发射部分和声学接收部分集成到一个紧凑的手持探头中，尺寸约为160毫米×64毫米×40毫米。此外，...

The goal of this paper is to develop a new skin imaging modality which addresses the current clinical need for a non-invasive imaging tool that images the skin over its depth with high resolutions while offering large histopathological-like contrasts between malignant and normal tissues. We demonstrate that by taking advantage of the intrinsic millimeter-wave dielectric contrasts between normal an...

本文旨在开发一种新的皮肤成像模态，以满足当前临床对非侵入性成像工具的需求，该工具能够以高分辨率对皮肤进行深度成像，并在恶性组织和正常组织之间提供类似组织病理学的大对比度。我们证明，通过利用正常组织和恶性组织之间固有的毫米波介电常数对比度，...

Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the f...

磁粒子成像（MPI）是一种相对较新的医学成像模态，它检测暴露于外部磁场的磁性纳米颗粒（MNPs）的非线性响应。MPI图像重建的系统矩阵（SM）方法在图像采集前需要进行耗时的系统校准扫描，其中在每个体素位置测量单个MNP样本，位于f...

The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer’s disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascu...

大脑通过类淋巴清除系统清除废物，其作用类似于身体其他部位的淋巴系统。在阿尔茨海默病（AD）中，类淋巴脑清除功能的损害先于蛋白质积累和认知功能下降。心血管搏动是类淋巴脑清除的主要驱动力。我们开发了一种量化心血管...的方法。

Radio-frequency (RF) arrays constructed using electric dipoles have potential benefits for transmit and receive applications using the ultra-high field (UHF) MRI. This paper examines some of the implementation barriers regarding dipole RF arrays for human head imaging at 7 T. The dipole array was constructed with conformal, meandered dipoles with dimensions selected utilizing an evolutionary-based...

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Hyperpolarized 129Xe magnetic resonance imaging is a powerful modality capable of assessing lung structure and function. While it has shown promise as a clinical tool for the longitudinal assessment of lung function, its utility as an investigative tool for animal models of pulmonary diseases is limited by the necessity of invasive intubation and mechanical ventilation procedures. In this paper, w...

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This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA) for higher-order total variation (HOTV) regularized positron emission tomography (PET) image reconstruction. The PET reconstruction problem can be formulated as a three-term convex optimization model consisting of the Kullback–Leibler (KL) fidelity term, a nonsmooth penalty term, an...

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Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if it is considered with respect to suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Po...

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Soft-tissue deformation presents a confounding factor to rigid image registration by introducing image content inconsistent with the underlying motion model, presenting non-correspondent structure with potentially high power, and creating local minima that challenge iterative optimization. In this paper, we introduce a model for registration performance that includes deformable soft tissue as a po...

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