Volume 36, Issue 7

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

数字显微组织图像中的细胞核分割能够提取高质量的特征，用于核形态学测量和计算病理学中的其他分析。传统的图像处理技术，如Otsu阈值分割和分水岭分割，在染色质稀疏和密集细胞核等具有挑战性的情况下效果不佳。相比之下，基于机器学习的分割可以泛化到各种细胞核外观。然而，训练机器学习算法需要大量已标注细胞核的图像数据集。公开可访问的标注数据集，以及广泛认可的用于比较技术的指标，已经极大地推动了其他图像分类问题（尤其是对象识别）的创新和进展。受其成功启发，我们引入了一个大型公开可访问的苏木精和伊红（H&E）染色组织图像数据集，其中包含超过21000个精心标注的细胞核边界，其质量由医学医生验证。由于我们的数据集来自多家医院，并包含来自多个患者、疾病状态和器官的多种细胞核外观，基于该数据集训练的技术很可能具有良好的泛化能力，并可直接应用于其他H&E染色图像。我们还提出了一种新的评估细胞核分割结果的指标，该指标统一惩罚对象级和像素级错误，而以往的指标只惩罚一种错误。我们还提出了一种基于深度学习的分割技术，特别强调识别细胞核边界，包括接触或重叠的细胞核之间的边界，并且在各种测试图像上表现良好。

Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem i...

机器人辅助手术（RAS）视频的理解是一个活跃的研究领域。建模外科医生的手势和技能水平是一个有趣的问题。获得的见解可应用于有效的技能获取、客观的技能评估、实时反馈以及人机协作手术。我们提出了一个解决工具检测与定位开放问题的方法...

Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the l...

从显微图像进行三维神经元结构的数字重建或追踪，是逆向工程大脑连接和结构的关键步骤。尽管已有许多尝试，但这一任务仍然非常具有挑战性，尤其是当图像受到噪声污染或存在神经突模式中断的情况时。解决这些问题的一种方法是识别...

Localization of anatomical structures is a prerequisite for many tasks in a medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3-D medical images through detection of their presence in 2-D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect the presence of the anatomical structure of interest...

解剖结构的定位是医学图像分析中许多任务的前提。我们提出了一种通过使用卷积神经网络（ConvNet）检测2D图像切片中解剖结构的存在，来自动定位3D医学图像中一个或多个解剖结构的方法。一个单一的ConvNet被训练来检测感兴趣的解剖结构的存在...

Magnetic particle imaging (MPI) is an emerging tomographic method that enables sensitive and fast imaging. It does not require ionizing radiation and thus may be a safe alternative for tracking of devices in the catheterization laboratory. The 3-D real-time imaging capabilities of MPI have been demonstrated in vivo and recent improvements in fast online image reconstruction enable almost real-time...

磁性粒子成像（MPI）是一种新兴的断层成像方法，能够实现灵敏且快速的成像。它不需要电离辐射，因此可能成为导管室中设备跟踪的安全替代方案。MPI的三维实时成像能力已在体内得到证明，并且快速在线图像重建的最新进展使得近乎实时的成像成为可能……

This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatoc...

本文提出了一种基于机器学习的自动分类方法，应用于使用声诺维造影剂的肝脏局灶性病变对比增强超声造影（CEUS）。该方法提取动脉期、门脉期、血管后期以及最大保持图像的空间和时间特征。病灶被分类为良性或恶性，再进一步分为良性、肝细胞...

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a s...

多模态数据融合最近作为一种全面的神经影像分析方法出现，通常使用典型相关分析（CCA）。然而，当前基于CCA的融合方法面临诸如高维性、多重共线性、单模态特征选择、不对称性以及将影像数据重塑为向量时空间信息丢失等问题。本文提出了一种...

In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due t...

在开放式图像引导肝脏手术（IGLS）中，可以获取术中器官表面的稀疏表示以驱动图像到物理的配准。我们假设，由于器官表面数据收集模式的变化引起的未表征误差限制了IGLS配准的准确性和鲁棒性。此类配准方法的临床验证面临挑战，因为...

Reliable identification of Human Epithelial-2 (HEp-2) cell patterns can facilitate the diagnosis of systemic autoimmune diseases. However, traditional approach requires experienced experts to manually recognize the cell patterns, which suffers from the inter-observer variability. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to simultan...

可靠识别人类上皮-2（HEp-2）细胞模式有助于系统性自身免疫性疾病的诊断。然而，传统方法需要经验丰富的专家手动识别细胞模式，这存在观察者间变异性问题。本文提出了一种基于全卷积网络（FCN）的自动模式识别系统，以同时...

Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts, and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always cap...

医学图像中病理结构的计算机分割具有挑战性，因为除了图像边界不清晰、图像伪影和手术活动痕迹外，病理结构的形状可能与非正常结构的形状有很大不同。即使收集了足够多的病理训练样本，统计形状建模也不能总是...

The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the data sets jointly as in data fusion or separately and then combining as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge. The primary reason for this is the difficulty in determining ...

从多个数据集中提取信息是许多学科固有的问题。可以通过数据融合的方式联合分析数据集，或者通过数据集成的方式分别分析再组合。然而，选择最佳方法组合和分析多数据集始终是一个挑战。主要原因是难以确定...

Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an explor...

分割透射光和/或荧光图像中的各类组织学结构是确定癌症病理基础、阐明肿瘤微环境中组织学结构之间的空间相互作用（例如肿瘤浸润淋巴细胞）、通过深度分子分析促进精准医学研究以及探索...的前提条件。

To avoid the confounding effects of anesthesia and immobilization stress in rat brain positron emission tomography (PET), motion tracking-based unrestrained awake rat brain imaging is being developed. In this paper, we propose a fast and accurate rat head motion tracking method based on small PET point sources. PET point sources (3-4) attached to the rat's head are tracked in image space using 15-...

为避免麻醉和固定应激对大鼠脑正电子发射断层扫描（PET）的混杂效应，基于运动追踪的无约束清醒大鼠脑成像正在被开发。本文提出了一种基于小PET点源的快速准确大鼠头部运动追踪方法。将3-4个PET点源附着在大鼠头部，在图像空间中使用15-...进行追踪。

Contrast source inversion electric properties tomography (CSI-EPT) is a recently developed technique for the electric properties tomography that recovers the electric properties distribution starting from measurements performed by magnetic resonance imaging scanners. This method is an optimal control approach based on the contrast source inversion technique, which distinguishes itself from other e...

对比源反演电特性断层成像（CSI-EPT）是一种最近开发的电特性断层成像技术，它从磁共振成像扫描仪进行的测量中恢复电特性分布。该方法是一种基于对比源反演技术的最优控制方法，其区别于其他...

Positron emission tomography (PET) relies on accurate timing information to pair two 511-keV photons into a coincidence event. Calibration of time delays between detectors becomes increasingly important as the timing resolution of detector technology improves, as a calibration error can quickly become a dominant source of error. Previous work has shown that the maximum likelihood estimate of these...

正电子发射断层扫描（PET）依赖于精确的时间信息来将两个511 keV光子配对成一个符合事件。随着探测器技术时间分辨率的提高，探测器之间时间延迟的校准变得越来越重要，因为校准误差可能迅速成为主要的误差来源。先前的工作表明，这些参数的最大似然估计...

The epithelium is a thin layer of tissue that lines hollow organs, such as colon. Visualizing in vertical cross sections with sub-cellular resolution is essential to understanding early disease mechanisms that progress naturally in the plane perpendicular to the tissue surface. The dual axes confocal architecture collects optical sections in tissue by directing light at an angle incident to the su...

上皮是覆盖在结肠等中空器官内壁的薄层组织。在垂直横截面上以亚细胞分辨率进行可视化对于理解在垂直于组织表面的平面上自然进展的早期疾病机制至关重要。双轴共焦结构通过将光以入射角照射到组织上来收集组织中的光学切片...

Multispectral imaging (MSI) can potentially assist the intra-operative assessment of tissue structure, function and viability, by providing information about oxygenation. In this paper, we present a novel technique for recovering intrinsic MSI measurements from endoscopic RGB images without custom hardware adaptations. The advantage of this approach is that it requires no modification to existing ...

多光谱成像（MSI）有可能通过提供氧合信息来辅助术中评估组织结构、功能和活力。在本文中，我们提出了一种新技术，无需定制硬件改造即可从内窥镜RGB图像中恢复内源性多光谱测量。该方法的优点是无需对现有硬件进行修改……

The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-to-noise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x-space approaches. To improve the signal-to-noise ra...

断层成像方法磁粒子成像（MPI）的时间分辨率非常高。对于信噪比低的测量电压信号，空间分辨率会降低，因为系统矩阵方法和x空间方法中的去卷积步骤需要增加图像重建步骤中的正则化。为了提高信噪比...

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that “smears” the currents coming from within the brain. Recording brain activi...

脑电图（EEG）是唯一的非侵入性、便携、被动、具有高时间分辨率且直接测量头皮电位的脑监测技术。EEG的一个主要缺点是空间分辨率低，这是由于低导电性的颅骨将来自大脑内部的电流“涂抹”开所致。记录大脑活动...

In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of m...

在最近发表的临床试验结果中，与标准癌症治疗相比，缺氧修饰疗法已显示为癌症患者提供更积极的结果。这些缺氧修饰疗法的开发和验证依赖于测量肿瘤缺氧的有效方法，但目前临床实践中尚无标准化的测量方法。不同类型的...

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