Volume 35, Issue 1

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

自动化核检测是许多计算机辅助病理相关图像分析算法（如乳腺癌组织标本的自动分级）的关键步骤。诺丁汉组织学评分系统与组织病理学图像中乳腺癌细胞核的形状和外观高度相关。然而，自动核检测面临两个挑战：1) 大量细胞核和高分辨率数字化病理图像的尺寸；2) 单个细胞核在大小、形状、外观和纹理上的变异性。近年来，“深度学习”策略在大图像数据的分类和分析中引起了关注。组织病理学由于其规模和复杂性，是应用深度学习策略的绝佳案例。本文提出了一种堆叠稀疏自动编码器（SSAE），作为一种深度学习策略的实例，用于在乳腺癌的高分辨率组织病理学图像上进行高效的细胞核检测。SSAE仅从像素强度中学习高层特征，以识别细胞核的区分性特征。对每幅图像应用滑动窗口操作，通过自编码器获取的高层特征表示图像块，然后将其输入分类器，将每个图像块分类为核或非核。在500张组织病理学图像（2200×2200）和约3500个手动分割的单个细胞核作为金标准的队列中，SSAE显示出改进的F-measure为84.49%，精确率-召回率曲线下的平均面积（AveP）为78.83%。SSAE方法还优于其他九种最先进的核检测策略。

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the...

本文提出了一种新的用于视网膜图像血管分割的监督方法。该方法将分割任务重新塑造为从视网膜图像到血管图的跨模态数据转换问题。提出了一种具有强归纳能力的宽深度神经网络来建模这种转换，并提出了一种高效的训练策略。

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to l...

计算机断层扫描（CT）成像是各种临床诊断和放射治疗计划中的重要工具。由于CT图像强度与正电子发射断层扫描（PET）衰减系数直接相关，因此对于PET图像的衰减校正（AC）不可或缺。然而，由于CT扫描中相对较高的辐射剂量，建议限制其使用。

We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented training databases that need to be recreate...

我们提出了一种新方法，利用迁移学习技术有效纠正稀疏标注在监督学习过程中引入的采样选择误差，以实现自动肿瘤分割。当前基于学习的自动化组织分类方法的实用性受到严重阻碍，因为它们依赖于需要重建的手动分割训练数据库...

The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computer-aided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottl...

本研究的目的是关于追踪神经元和视网膜图像中的丝状结构。在神经疾病药物筛选或糖尿病视网膜病变的计算机辅助诊断等应用中，识别神经元网络中的单个神经元或分离视网膜血管网络中的血管树结构通常至关重要。这两项任务都具有挑战性，因为相同的瓶颈...

The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automa...

从计算机断层扫描（CT）中准确分割肺病变对于肺癌研究非常重要，可以为临床诊断和治疗提供有价值的信息。然而，由于肺病变的异质性，实现具有可接受精度的全自动病变检测和分割具有挑战性。在这里，我们提出了一种基于雪橇增长自动...

Most X-ray fluoroscopy systems are stationary and impose restrictions on the measurement of dynamic joint motion; for example, knee-joint kinematics during gait is usually measured with the subject ambulating on a treadmill. We developed a computer-controlled, mobile, biplane, X-ray fluoroscopy system to track human body movement for high-speed imaging of 3D joint motion during overground gait. A ...

大多数X射线透视系统是固定式的，对动态关节运动的测量造成限制；例如，步态中的膝关节运动学通常是在受试者在跑步机上行走时测量的。我们开发了一种计算机控制的移动双平面X射线透视系统，用于跟踪人体运动，以在地面行走过程中高速成像三维关节运动。

Registration of images in the presence of intra-image signal fluctuations is a challenging task. The definition of an appropriate objective function measuring the similarity between the images is crucial for accurate registration. This paper introduces an objective function that embeds local phase features derived from the monogenic signal in the modality independent neighborhood descriptor (MIND)...

在存在图像内信号波动的情况下进行图像配准是一项具有挑战性的任务。定义一个合适的衡量图像之间相似性的目标函数对于精确配准至关重要。本文介绍了一个目标函数，该函数将单基因信号导出的局部相位特征嵌入到模态独立邻域描述符（MIND）中...

Purpose: Pathologists often look at whole slide images (WSIs) at low magnification to find potentially important regions and then zoom in to higher magnification to perform more sophisticated analysis of the tissue structures. Many automated methods of WSI analysis attempt to preprocess the down-sampled image in order to select salient regions which are then further analyzed by a more computationa...

目的：病理学家通常以低放大倍数观察全切片图像（WSI）以寻找潜在的重要区域，然后放大到更高放大倍数进行更复杂的组织结构分析。许多自动化的WSI分析方法尝试预处理下采样图像以选择显著区域，然后通过计算量更大的方法进一步分析这些区域。

This paper describes the development and evaluation of a new beamforming strategy based on pixel-based focusing for ultrasound linear array systems. We first implement conventional pixel-based beamforming in which the transmitted wave is assumed as spherical and diverging from the centre of the transmit subaperture. This assumed wave-shape is only valid within a limited angle on each side of the b...

本文介绍了一种基于像素聚焦的超声线性阵列系统波束形成新策略的开发与评估。我们首先实现了传统的像素波束形成，其中假设发射波为球形，并从发射子孔径中心发散。这种假设的波形仅在每个b...的有限角度内有效。

Atherosclerosis and Abdominal Aortic Aneurysms (AAAs) are two common vascular diseases associated with mechanical changes in the arterial wall. Pulse Wave Imaging (PWI), a technique developed by our group to assess and quantify the mechanical properties of the aortic wall in vivo, may provide valuable diagnostic information. This work implements piecewise PWI (pPWI), an enhanced version of PWI des...

动脉粥样硬化和腹主动脉瘤（AAA）是与动脉壁力学改变相关的两种常见血管疾病。脉冲波成像（PWI）是我们团队开发的一种技术，用于评估和量化体内主动脉壁的力学特性，可能提供有价值的诊断信息。本工作实现了分段脉冲波成像（pPWI），这是PWI的增强版本，用于...

This work provides an insight into positron emission tomography (PET) joint image reconstruction/motion estimation (JRM) by maximization of the likelihood, where the probabilistic model accounts for warped attenuation. Our analysis shows that maximum-likelihood (ML) JRM returns the same reconstructed gates for any attenuation map ($\mu$ -map) that is a deformation of a given $\mu$-map, regardless ...

这项工作通过最大化似然函数，深入探讨了正电子发射断层扫描（PET）联合图像重建/运动估计（JRM），其中概率模型考虑了扭曲衰减。我们的分析表明，对于任何通过变形给定μ图得到的衰减图（μ-map），最大似然（ML）JRM返回相同的重建门控，无论...

The theory and techniques of compressed sensing (CS) have shown their potential as a breakthrough in accelerating $k$-space data acquisition for parallel magnetic resonance imaging (pMRI). However, the performance of CS reconstruction models in pMRI has not been fully maximized, and CS recovery guarantees for pMRI are largely absent. To improve reconstruction accuracy from parsimonious amounts of ...

压缩感知（CS）的理论和技术已显示出作为加速并行磁共振成像（pMRI）中k空间数据采集的突破性潜力。然而，CS重建模型在pMRI中的性能尚未完全最大化，并且pMRI的CS恢复保证基本缺失。为了提高从少量数据中重建的准确性...

Lack of classifier robustness is a barrier to widespread adoption of computer-aided diagnosis systems for computed tomography (CT). We propose a novel Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features robust to variations in CT technical factors. We evaluated RDFS in CT classification of fibrotic interstitial lung disease using 3D texture features. CTs were ...

分类器鲁棒性不足是计算机断层扫描（CT）计算机辅助诊断系统广泛应用的障碍。我们提出了一种新颖的基于鲁棒性的特征选择（RDFS）算法，该算法优先选择对CT技术因素变化具有鲁棒性的特征。我们使用三维纹理特征在CT分类纤维化间质性肺病中评估了RDFS。CT扫描...

Magnetic Resonance-Electrical Properties Tomography (MR-EPT) is an imaging modality that maps the spatial distribution of the electrical conductivity and permittivity using standard MRI systems. The presence of a body within the scanner alters the RF field, and by mapping these alterations it is possible to recover the electrical properties. The field is time-harmonic, and can be described by the ...

磁共振-电特性断层成像（MR-EPT）是一种利用标准MRI系统映射电导率和介电常数空间分布的成像模态。扫描仪内身体的存在会改变射频场，通过映射这些变化，可以恢复电特性。该场是时谐的，可以用...

Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in cli...

尽管实体瘤疗效评价标准（RECIST）是目前评估治疗后实体瘤大小变化的临床指南，但其与患者无进展生存期（PFS）临床结局的关联性较低。本文提出了一种基于可变形图像配准的新方法，用于评估卵巢癌患者对新化疗药物的反应在临床...

Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images by extracting hundreds of image features and using them to predict disease presence or outcome. Since constructing a robust and interpretable classifier is challenging in a high dimensional feature space, dimensionality reduction (DR) is often implemented prior to cla...

定量组织形态测量学（QH）指的是通过提取数百个图像特征并利用它们预测疾病存在或结果，对数字病理图像进行疾病外观计算建模的过程。由于在高维特征空间中构建稳健且可解释的分类器具有挑战性，通常在分类之前实施降维（DR）...

There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features. ...

最近，人们开始关注使用功能磁共振成像（fMRI）来解码更自然、认知的状态，在这种状态下，受试者以连续、自主的方式执行各种任务。在这种情况下，通常将整个任务期间的脑体积集作为一个样本，并使用连通性估计（如皮尔逊相关）作为特征。...

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The applicatio...

利用MRI检查中多幅图像之间的冗余可以形式化为这些图像的联合重建。解剖结构得以保留，因此可以实施特定的约束（例如，大多数特征或空间梯度应在所有图像中位于同一位置），并且只需要编码从一幅图像到另一幅图像的对比度变化。该应用...

In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem du...

在图像引导机器人手术中，将内窥镜视频流分割成有意义的部分提供了重要的上下文信息，外科医生可以利用这些信息来增强对手术场景的感知。这些信息为外科医生在执行关键任务（如组织切割）之前提供实时决策指导。内窥镜视频分割是一个具有挑战性的问题，由于...

We theoretically demonstrated a new optical imaging technique based on reverse-time migration (RTM) for reconstructing optical structures in homogeneous media for the first time. RTM is a powerful wave-equation-based method to reconstruct the image of the structure by modeling the wave propagation inside the media with both forward modeling and reverse-time extrapolation. While RTM is commonly use...

我们首次从理论上展示了一种基于逆时偏移（RTM）的新型光学成像技术，用于重建均匀介质中的光学结构。RTM是一种基于波动方程的强大方法，通过正向建模和逆时外推模拟介质内的波传播来重建结构图像。虽然RTM通常用于...

The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction...

高密度物体的存在仍然是医学CT成像中的一个开放性问题。通过高密度物体（如金属植入物）的投影数据受噪声主导，并且受到束硬化及散射的强烈影响。重建图像因出现明显的暗条纹和亮条纹伪影而降低诊断可靠性。一种新的重建方法...

Extracting geometrical information from large 2D or 3D biomedical images is important to better understand fundamental phenomena such as morphogenesis. We address the problem of automatically analyzing spatial organization of cells or nuclei in 2D or 3D images of tissues. This problem is challenging due to the usually low quality of microscopy images as well as their typically large sizes. The str...

从大型二维或三维生物医学图像中提取几何信息对于更好地理解诸如形态发生等基本现象非常重要。我们解决了自动分析二维或三维组织图像中细胞或细胞核空间组织的问题。由于显微镜图像通常质量较低且尺寸较大，这个问题具有挑战性。

Noninvasive cardiac electrophysiological (EP) imaging aims to mathematically reconstruct the spatiotemporal dynamics of cardiac sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, a fixed-model approach enforces the source distribution to follow a pre-assumed structure that does not always match the varying ...

无创心脏电生理成像旨在从体表心电图数据中数学重建心脏源的时空动态。这个不适定问题通常通过一个固定的约束模型进行正则化。然而，固定模型方法强制源分布遵循一个预先假定的结构，该结构并不总是与变化的...相匹配

As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We repres...

随着右心室在心血管疾病中的作用被更广泛地认识，对右心室成像、功能和量化的兴趣正在增长。然而，目前在文献或商业应用中针对三维超声心动图的右心室量化方法很少。本文提出了一种用于三维超声心动图图像的自动右心室分割方法。我们...