Volume 36, Issue 4

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

皮肤镜图像中的黑色素瘤自动识别是一项极具挑战性的任务，原因在于皮肤病变对比度低、黑色素瘤类内差异大、黑色素瘤与非黑色素瘤病变视觉相似度高以及图像中存在大量伪影。为应对这些挑战，我们提出了一种利用非常深卷积神经网络（CNN）进行黑色素瘤识别的新方法。与现有使用低级手工特征或浅层CNN的方法相比，我们的深层网络（超过50层）能够获取更丰富、更具判别性的特征，从而实现更准确的识别。为充分利用非常深网络，我们提出了一系列方案来确保在有限训练数据下进行有效训练和学习。首先，我们应用残差学习来处理网络加深时的退化和过拟合问题。该技术可确保我们的网络因深度增加而受益于性能提升。然后，我们构建了一个全卷积残差网络（FCRN）用于精确的皮肤病变分割，并通过集成多尺度上下文信息方案进一步增强其能力。最后，我们将提出的FCRN（用于分割）与其他非常深残差网络（用于分类）无缝集成，形成两阶段框架。该框架使分类网络能够基于分割结果而非整个皮肤镜图像提取更具代表性和特异性的特征，进一步缓解了训练数据不足的问题。该框架在ISBI 2016皮肤病变分析向黑色素瘤检测挑战数据集上进行了广泛评估。实验结果表明，所提框架性能显著提升，在分类和分割任务中分别位列25个团队和28个团队中的第一和第二。本研究证实，即使训练数据有限，具有有效训练机制的非常深CNN也能用于解决复杂的医学图像分析任务。

In the present proof of principle study, we evaluated the homogenous angular spectrum method for passive acoustic mapping (AS-PAM) of microbubble oscillations using simulated and experimental data. In the simulated data we assessed the ability of AS-PAM to form 3D maps of a single and multiple point sources. Then, in the two dimensional limit, we compared the 2D maps from AS-PAM with alternative f...

在目前的概念验证研究中，我们利用模拟和实验数据评估了均匀角谱法用于微泡振荡的被动声学映射（AS-PAM）。在模拟数据中，我们评估了AS-PAM形成单个和多个点源三维图的能力。然后，在二维极限下，我们将AS-PAM的二维图与替代方法进行了比较...

A rotational Electrical Impedance Tomography (rEIT) methodology is described and shown to produce spatially accurate absolute reconstructions with improved image contrast and an improved ability to distinguish closely spaced inclusions compared to traditional EIT on data recorded from cylindrical and breast-shaped tanks. Rotations of the tank without altering the interior conductivity distribution...

描述了一种旋转电阻抗层析成像（rEIT）方法，并表明与传统的EIT相比，在圆柱形和乳房形水箱上记录的数据中，该方法能产生空间精确的绝对重建，具有更好的图像对比度和区分紧密间隔内含物的能力。水箱的旋转不改变内部电导率分布...

A novel beamforming technique, named compressed sensing based synthetic transmit aperture (CS-STA) is proposed to speed up the acquisition of ultrasound imaging. This technique consists of three steps. First, the ultrasound transducer transmits randomly apodized plane waves for a number of times and receives the backscattered echoes. Second, the recorded backscattered echoes are used to recover th...

提出了一种新颖的波束形成技术，称为基于压缩感知的合成发射孔径（CS-STA），以加速超声成像的采集。该技术包括三个步骤。首先，超声换能器多次发射随机变迹平面波并接收背向散射回波。其次，记录的背向散射回波用于恢复...

We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidate...

我们提出了一种新颖的方法，用于在时间序列图像中自动跟踪椭圆细胞群。给定初始分割，我们通过生成过完备的竞争检测假设来处理部分遮挡和重叠。为此，我们将椭圆拟合到初始区域的部分，并构建椭圆层次结构，然后将其视为细胞候选...

The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to alleviate the limitations of small datasets. We have previously developed an image blending tool that allow...

分类器的性能在很大程度上取决于用于训练的数据的大小和代表性。在训练样本的积累和/或标记困难或昂贵的情况下，例如医学应用，数据增强可用于缓解小数据集的局限性。我们之前开发了一种图像混合工具，允许...

Finite element methods have been shown to achieve high accuracies in numerically solving the EEG forward problem and they enable the realistic modeling of complex geometries and important conductive features such as anisotropic conductivities. To date, most of the presented approaches rely on the same underlying formulation, the continuous Galerkin (CG)-FEM. In this article, a novel approach to so...

有限元方法已被证明在数值求解脑电图正问题时具有高精度，并且能够对复杂几何形状和重要的导电特征（如各向异性电导率）进行真实建模。迄今为止，大多数提出的方法都依赖于相同的基本公式，即连续伽辽金有限元法（CG-FEM）。在本文中，一种新颖的方法...

The development of ultrafast ultrasound imaging brings great opportunities to improve imaging technologies such as shear wave elastography and ultrafast Doppler imaging. In ultrafast imaging, several tilted plane or diverging wave images are coherently combined to form a compounded image, leading to trade-offs among image signal-to-noise ratio (SNR), resolution, and post-compounded frame rate. Mul...

超快超声成像的发展为改善剪切波弹性成像和超快多普勒成像等成像技术带来了巨大机遇。在超快成像中，多个倾斜平面波或发散波图像被相干组合以形成复合图像，这导致图像信噪比（SNR）、分辨率和复合后帧率之间的权衡。多...

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden-angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the ${k}$ -space data for more accurate motion...

我们提出了一种基于流形对齐（MA）的新型回顾性自门控方法，该方法能够重建自由呼吸、高空间和时间分辨率的腹部磁共振成像序列。基于径向黄金角采集轨迹，我们的方法能够从k空间数据中提取多维自门控信号，以实现更准确的运动校正。

Respiratory signals are required for image gating and motion compensation in minimally invasive interventions. In X-ray fluoroscopy, extraction of a respiratory signal can be challenging due to characteristics of interventional imaging, in particular injection of contrast agent and automatic exposure control. We present a novel method for respiratory signal extraction based on dimensionality reduc...

在微创介入手术中，呼吸信号对于图像门控和运动补偿是必需的。在X射线透视中，由于介入成像的特性，特别是造影剂注射和自动曝光控制，呼吸信号的提取可能具有挑战性。我们提出了一种基于降维的新颖呼吸信号提取方法...

Proton resonance frequency shift-based magnetic resonance thermometry is a currently used technique for monitoring temperature during targeted thermal therapies. However, in order to provide temperature updates with very short latency times, fast MR acquisition schemes are usually employed, which in turn might lead to noisy temperature measurements. This will, in general, have a direct impact on t...

基于质子共振频率偏移的磁共振测温是目前用于监测靶向热疗过程中温度的技术。然而，为了提供极短延迟时间的温度更新，通常采用快速MR采集方案，这可能导致温度测量噪声较大。一般来说，这将对...

The Hotelling Observer (HO) is widely used to evaluate image quality in medical imaging. However, applying it to data that are not multivariate-normally (MVN) distributed is not optimal. In this paper, we apply two multi-template linear observer strategies to handle such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Nex...

霍特林观察者（HO）被广泛用于评估医学成像中的图像质量。然而，将其应用于非多元正态分布的数据并非最优。本文中，我们应用了两种多模板线性观察者策略来处理此类数据。首先，将整个数据集合划分为精确或近似多元正态且同方差的子集合。接下来...

Preterm neonates with a very low birth weight of less than 1,500 grams are at increased risk for developing intraventricular hemorrhage (IVH), which is a major cause of brain injury in preterm neonates. Quantitative measurements of ventricular dilatation or shrinkage play an important role in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ven...

出生体重低于1500克的极低出生体重早产儿发生脑室内出血（IVH）的风险增加，IVH是早产儿脑损伤的主要原因。脑室扩张或缩小的定量测量在监测患者和评估治疗方案中起着重要作用。三维超声（US）已被开发用于监测脑室...

Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity and it can be particularly useful in early detection and grading of arthritis. In a recent study we have shown that a Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can...

对比增强超声（CEUS）是一种敏感的成像技术，用于评估组织血管分布，在关节炎的早期检测和分级中特别有用。在最近的一项研究中，我们证明伽马变量可以准确量化滑膜灌注，并且足够灵活以描述许多异质性模式。然而，在某些情况下，动力学的异质性可能...

Optical Diffusion Tomography (ODT) is a modern non-invasive medical imaging modality which requires mathematical modelling of near-infrared light propagation in tissue. Solving the ODT forward problem equation accurately and efficiently is crucial. Typically, the forward problem is represented by a Diffusion PDE and is solved using the Finite Element Method (FEM) on a mesh, which is often unstruct...

光学扩散断层成像(ODT)是一种现代无创医学成像模态,需要对组织中近红外光传播进行数学建模。准确高效地求解ODT正问题方程至关重要。通常,正问题由扩散PDE表示,并在网格上使用有限元法(FEM)求解,这种网格通常是非结构化...

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