Volume 36, Issue 11

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

在二维超声中期妊娠检查中，识别和解释胎儿标准扫描切面是非常复杂的任务，需要多年的训练。除了引导探头到正确位置外，非专家同样难以识别图像中的相关结构。自动图像处理可以为经验丰富和缺乏经验的操作者提供工具来帮助完成这些任务。在本文中，我们提出了一种基于卷积神经网络的新方法，该方法可以在手持式二维超声数据中自动检测13个胎儿标准视图，并通过边界框提供胎儿结构的定位。一个重要的贡献是，网络仅基于图像级标签的弱监督学习来定位目标解剖结构。网络架构设计为实时运行，同时为定位任务提供最佳输出。我们展示了实时标注、从保存视频中回顾性帧检索以及在一个由完整临床异常筛查的图像和视频记录组成的非常大且具有挑战性的数据集上的定位结果。我们发现，在模拟实时检测的现实分类实验中，所提方法达到了0.798的平均F1分数，回顾性帧检索的准确率为90.09%。此外，定位任务的准确率达到77.8%。

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm a...

在本文中，我们开发了一种新的弱监督学习算法，用于学习分割组织病理学图像中的癌变区域。本文采用多实例学习（MIL）框架，提出了一种新的公式——深度弱监督（DWS）；我们还提出了一种有效的方法，向神经网络引入约束以辅助学习过程。我们的算法的贡献包括...

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatom...

脑提取或全脑分割是许多神经影像分析流程中的重要第一步。因此，脑提取的准确性和鲁棒性对于整个脑分析过程的准确性至关重要。最先进的脑提取技术严重依赖于脑图谱与查询脑解剖之间的对齐或配准的准确性...

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of brea...

我们描述了一种自动化方法，用于分析未配准的头尾（CC）和内外斜（MLO）乳腺X线摄影视图，以估计患者患乳腺癌的风险。该方法的主要创新在于使用深度学习模型来联合分类未配准的乳腺X线摄影视图及其相应的分割图...

This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3-D-MRP, is based on the principle of minimum rate predictors (MRPs), which is one of the state-of-the-art lossless compression technologies presented in the data compression literature. The main features of the proposed method inclu...

本文描述了一种对体积医学图像（如CT或MRI）进行无损压缩的高效方法。所提出的方法称为3-D-MRP，基于最小速率预测器（MRP）原理，这是数据压缩文献中提出的最先进的无损压缩技术之一。该方法的主要特点包括...

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentatio...

心脏容积/功能评估仍是日常心脏病学中的关键步骤，3D超声发挥着越来越重要的作用。然而，由于超声成像的伪影和低对比度噪声比，全自动左心室分割是一项具有挑战性的任务。本文提出了一种快速且全自动的框架，用于全周期心内膜左心室分割...

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, ...

虽然已经开发了许多基于低秩和稀疏的方法用于加速动态磁共振成像(dMRI)，但它们都在输入空间中使用低秩性或稀疏性，忽略了大多数dMRI数据中固有的非线性相关性。在本文中，我们提出了一种基于核的框架，允许在欠奈奎斯特数据重建中使用非线性流形模型。在这个框架内，...

Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weigh...

生物发光断层扫描(BLT)是一种强大的非侵入性分子成像工具，用于小鼠胶质瘤的体内研究。然而，由于光散射及其导致的不适定问题，开发一种能够准确定位肿瘤并在三维空间中定义肿瘤形态的充分重构方法具有挑战性。在本文中，我们提出了一种新颖的高斯加权...

SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During ...

SCoTS通过训练库中先前描绘的形状的线性张成，捕获输入图像中形状的稀疏表示。该模型在水平集迭代过程中更新形状先验，并通过训练数据的稀疏组合捕获形状的变异性。因此，水平集演化由数据项和捕获有效先验形状的项共同驱动。在……过程中……

The objective of this paper is to develop a noncontact, noninvasive system for detecting and monitoring subcutaneous infection occurring at the tissue and osseointegrated prosthesis interface. It is known that the local pH of tissue can change due to infection. Therefore, the sensing system integrates two parts, namely, pH-sensitive thin films that can be coated onto prosthesis surfaces prior to t...

本文旨在开发一种非接触式、无创系统，用于检测和监测发生在组织与骨整合假体界面的皮下感染。已知感染会导致组织局部pH值变化。因此，传感系统集成了两个部分，即pH敏感薄膜（可在植入前涂覆于假体表面）和...

The combination of medical imaging with computational fluid dynamics (CFD) has enabled the study of 3-D blood flow on a patient-specific level. However, with models based on gated high-resolution data, the study of transient flows, and any model implementation into routine cardiac care, is challenging. This paper presents a novel pathway for patient-specific CFD modelling of the left ventricle (LV...

医学成像与计算流体动力学（CFD）的结合使得在患者特异性水平上研究三维血流成为可能。然而，基于门控高分辨率数据的模型，对于瞬态血流的研究以及任何模型在常规心脏护理中的应用，都充满挑战。本文提出了一种新颖的路径，用于左心室（LV）的患者特异性CFD建模...

Photon counting detectors (PCDs) provide multiple energy-dependent measurements for estimating basis line-integrals. However, the measured spectrum is distorted from the spectral response effect (SRE) via charge sharing, K-fluorescence emission, and so on. Thus, in order to avoid bias and artifacts in images, the SRE needs to be compensated. For this purpose, we recently developed a computationall...

光子计数探测器（PCD）提供多个能量相关测量用于估计基线条积分。然而，由于电荷共享、K荧光发射等光谱响应效应（SRE），测量光谱会发生畸变。因此，为了避免图像中的偏差和伪影，需要对SRE进行补偿。为此，我们最近开发了一种计算上...

A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation erro...

提出了一种全自动的二维+时间心肌分割框架，用于心脏磁共振（CMR）血氧水平依赖（BOLD）数据集。使用CINE BOLD CMR进行缺血检测依赖于心肌强度的时空模式，但这些模式也困扰着监督分割方法，而监督分割方法是电影MRI中心肌分割的事实标准。分割错误...

This paper proposes a new method of simulating dose reduction in digital breast tomosynthesis, starting from a clinical image acquired with a standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the method accounts for pixel crosstalk, which causes the noise to be frequency-dependent, thus increasing the simulation accuracy. For ...

本文提出了一种在数字乳腺断层合成中模拟剂量减少的新方法，该方法从使用标准辐射剂量获得的临床图像开始。它同时考虑了信号相关的量子噪声和信号无关的电子噪声。此外，该方法还考虑了像素串扰，这导致噪声具有频率依赖性，从而提高了模拟的准确性。对于...

Whole body oncological screening using CT images requires a good anatomical localisation of organs and the skeleton. While a number of algorithms for multi-organ localisation have been presented, developing algorithms for a dense anatomical annotation of the whole skeleton, however, has not been addressed until now. Only methods for specialised applications, e.g., in spine imaging, have been previ...

使用CT图像进行全身肿瘤筛查需要对器官和骨骼进行良好的解剖定位。尽管已经提出了许多用于多器官定位的算法，但开发用于整个骨骼密集解剖注释的算法至今尚未得到解决。只有针对专门应用的方法，例如脊柱成像，先前已有研究...

In fluorescence optical tomography, many works in the literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructio...

在荧光光学断层扫描中，文献中的许多工作关注于线性重建问题以获取荧光产额，或线性化重建问题以获取吸收系数。非线性重建问题——重建荧光团吸收系数——在成像研究中备受关注，因为它提供了更好重建的可能性...

Patients with drug-refractory heart failure can greatly benefit from cardiac resynchronization therapy (CRT). A CRT device can resynchronize the contractions of the left ventricle (LV) leading to reduced mortality. Unfortunately, 30%-50% of patients do not respond to treatment when assessed by objective criteria such as cardiac remodeling. A significant contributing factor is the suboptimal placem...

药物难治性心力衰竭患者可以显著受益于心脏再同步化治疗（CRT）。CRT装置可以重新同步左心室（LV）的收缩，从而降低死亡率。不幸的是，根据客观标准如心脏重塑评估，30%-50%的患者对治疗无反应。一个重要影响因素是次优的放置...

Blood sampling is the most common medical technique, and vessel detection is of crucial interest for automated venipuncture systems. In this paper, we propose a new convex-regional-based gradient model that uses contextually related regional information, including vessel width size and gray distribution, to segment and locate vessels in a near-infrared image. A convex function with the interval si...

采血是最常见的医疗技术，血管检测对于自动静脉穿刺系统至关重要。本文提出了一种新的基于凸区域的梯度模型，该模型利用上下文相关的区域信息，包括血管宽度和灰度分布，在近红外图像中分割和定位血管。具有区间si...

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描述上述即将举行的会议活动。可能包括涵盖的主题或征稿通知。

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