Volume 37, Issue 11

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。

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms.

青光眼是一种导致不可逆视力丧失的慢性眼病。现有的自动筛查方法大多先分割主要结构，然后计算临床指标以检测和筛查青光眼。然而，这些基于测量的方法严重依赖于分割精度，并忽略了各种视觉特征。在本文中，我们介绍...

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers...

卷积神经网络在过去几年中彻底改变了医学图像分析。U-Net架构是最知名的语义分割卷积神经网络架构之一，在许多不同的医学图像分割应用中取得了显著成功。U-Net架构由标准卷积层、池化层和上采样层组成...

Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time consuming, difficult to standardize, and thus not feasible for large clinical studie...

近期研究表明，肺动脉血管疾病可能通过不同的生理机制分别影响动脉或静脉。为了检测两个血管树的变化，医生手动分析患者的胸部计算机断层扫描（CT）图像以寻找异常。这个过程耗时、难以标准化，因此不适合大规模临床研究...

A novel approach to tomographic data processing has been developed and evaluated using the Jagiellonian positron emission tomography scanner as an example. We propose a system in which there is no need for powerful, local to the scanner processing facility, capable to reconstruct images on the fly. Instead, we introduce a field programmable gate array system-on-chip platform connected directly to ...

以亚捷隆正电子发射断层扫描仪为例，开发并评估了一种新颖的断层数据处理方法。我们提出了一种系统，无需在扫描仪本地配备强大的处理设施，能够即时重建图像。相反，我们引入了一种直接连接到...的现场可编程门阵列片上系统平台。

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration, and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyze the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal im...

视网膜眼底照片已被用于诊断许多眼部疾病，如青光眼、病理性近视、年龄相关性黄斑变性和糖尿病视网膜病变。随着计算机科学的发展，计算机辅助诊断已被开发用于自动处理和分析视网膜图像。分析中的一个挑战是视网膜图像的质量...

The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main...

从显微图像中自动重建单个神经元对于实现大规模数据驱动的神经元形态研究至关重要。然而，以往很少有方法能够在无需人工干预的情况下，从三维显微图像中自动生成令人满意的结果。本文提出了一种新的自动三维神经元重建算法。主要...

Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents a novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization proble...

结构化低秩矩阵模型先前已被引入，以便从亚奈奎斯特数据实现无校准的MR图像重建，并且这些思想最近被扩展以实现无导航仪回波平面成像（EPI）鬼影校正。本文提出了一种新的理论分析，表明由于均匀子采样，结构化低秩矩阵优化问题...

High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HF...

高频振荡（HFOs）是自发脑磁图（MEG）模式，已被认为是识别癫痫灶的潜在生物标志物。正确检测MEG信号中的HFOs对于准确及时的临床评估至关重要。由于HFOs的视觉检查耗时、易出错且审查者间可靠性差，因此自动高频...

Over the years, medical image tracking has gained considerable attention from both medical and research communities due to its widespread utility in a multitude of clinical applications, from functional assessment during diagnosis and therapy planning to structure tracking or image fusion during image-guided interventions. Despite the ever-increasing number of image tracking methods available, mos...

多年来，医学图像跟踪因其在众多临床应用中的广泛实用性而受到医学界和研究界的广泛关注，从诊断和治疗规划中的功能评估到图像引导干预中的结构跟踪或图像融合。尽管可用的图像跟踪方法数量不断增加，但大多数...

In this study, we developed and validated a Geant4-based Monte Carlo (MC) model of an experimental benchtop X-ray fluorescence (XRF) computed tomography (XFCT) system for quantitative imaging of metallic nanoparticles such as gold nanoparticles (GNPs) injected into small animals for preclinical testing of various NP-based diagnostic and therapeutic approaches. Detailed hardware components of the c...

在本研究中，我们开发并验证了一个基于Geant4的蒙特卡罗（MC）模型，该模型用于实验性台式X射线荧光（XRF）计算机断层扫描（XFCT）系统，以实现金属纳米颗粒（如金纳米颗粒（GNP））的定量成像，这些纳米颗粒被注入小动物体内，用于各种基于纳米颗粒的诊断和治疗方法的临床前测试。详细描述了系统的硬件组件，包括X射线源、准直器、探测器等。该模型用于模拟XRF信号的产生和探测，并应用于验证一种基于反卷积的信号提取方法，该方法旨在从测量的荧光信号中去除散射和噪声影响。结果表明，该MC模型能够准确模拟XFCT系统的性能，为后续实验设计和数据处理提供了有力工具。

In quantitative magnetic resonance T1 mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution T1 weighted images in a clinically feasible time. Fast, linear methods that estimate T1 maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squar...

在定量磁共振T1映射中，可变翻转角（VFA）稳态破坏梯度回波（SPGR）成像技术因其能够在临床可行时间内提供一系列高分辨率T1加权图像而广受欢迎。已经提出了从这些加权图像估计T1图的快速线性方法，例如DESPOT1和迭代重加权线性最小二乘...

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constrai...

预测模型可用于高维脑图像来解码认知状态或临床状况/演变的诊断/预后。通过结构化稀疏进行空间正则化在这种情况下提供了新的视角，并通过迫使解遵循领域特定的约束来降低模型过拟合的风险，同时提供可解释的神经影像特征...

Biomedical parameters of tissue can be important indicators for clinical diagnosis. One such parameter that reflects tissue stiffness is elasticity, the imaging of which is called elastography. In this paper, we use displacements from harmonic excitations to solve the inverse problem of elasticity based on a finite-element method (FEM) formulation. This leads to iterative solution of nonlinear and...

生物组织的生物医学参数可以是临床诊断的重要指标。反映组织硬度的一个参数是弹性，其成像称为弹性成像。在本文中，我们利用谐波激励产生的位移，基于有限元法公式求解弹性的逆问题。这导致了非线性的迭代解和...

Identification of objective criteria to correctly diagnose ectatic diseases of the cornea or to detect early stages of corneal ectasia is of great interest in ophthalmology and optometry. Metrics for diagnosis typically employed are curvature maps (axial/sagittal, tangential); elevation map of the anterior surface of the cornea with respect to a reference sphere; and pachymetry (thickness) map of ...

在眼科和视光学中，识别客观标准以正确诊断角膜扩张性疾病或检测角膜扩张的早期阶段具有重要意义。常用的诊断指标包括曲率图（轴向/矢状、切向）、角膜前表面相对于参考球面的高程图以及角膜厚度图等。

The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special ty...

人脑是一个极其复杂的网络。该网络中的异常活动可能导致多种神经疾病，如多发性硬化症、帕金森病、阿尔茨海默病和自闭症。功能磁共振成像已成为描绘受此类疾病（尤其是自闭症）影响神经回路的重要工具。本文提出了一种特殊的...

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