Volume 38, Issue 4

The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3-D lung nodule characteristics by decomposing a 3-D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules' overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.

在胸部CT上准确识别恶性肺结节对于肺癌的早期检测至关重要，这也为患者提供了最佳治愈机会。尽管深度学习方法最近已成功引入计算机视觉问题，但由于缺乏大规模训练数据集，恶性结节的检测仍面临巨大挑战。本文提出了一种基于多视图知识的协作（MV-KBC）深度模型，利用有限的胸部CT数据区分恶性和良性结节。我们的模型通过将3D结节分解为九个固定视图来学习3D肺结节特征。对于每个视图，我们构建一个基于知识的协作（KBC）子模型，其中设计了三种图像块，分别微调三个预训练的ResNet-50网络，以表征结节的整体外观、体素和形状异质性。我们联合使用九个KBC子模型对肺结节进行分类，并在误差反向传播过程中学习自适应加权方案，使MV-KBC模型能够以端到端的方式进行训练。使用惩罚损失函数以减少假阴性率，同时最小化对MV-KBC模型整体性能的影响。我们在基准LIDC-IDRI数据集上测试了该方法，并与五种最先进的分类方法进行了比较。结果表明，MV-KBC模型在肺结节分类中达到了91.60%的准确率，AUC为95.70%。这些结果明显优于现有最先进的方法。

A key limitation of deep convolutional neural network (DCNN)-based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation ...

基于深度卷积神经网络（DCNN）的图像分割方法的一个关键局限性是缺乏泛化能力。在对新的成像模态或不同疾病队列中的器官进行分割时，通常需要手动勾勒训练图像。如果一种成像模态（如MRI）中的手动勾勒图像能够训练分割网络，则可以减轻手动工作……

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propo...

在本文中，我们提出了一种新的深度学习框架，用于解剖分割和自动标志点检测。具体来说，我们专注于从锥束计算机断层扫描（CBCT）中分割下颌骨并在测地空间中识别9个下颌骨解剖标志点的挑战性问题。整体方法包含三个相互关联的步骤。第一步，我们提出...

Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeli...

准确的手术时长估计对于优化手术室规划至关重要，这对患者舒适度和安全性以及资源优化具有重要作用。然而，术前预测手术时长具有挑战性，因为它根据患者状况、外科医生技能和术中情况而显著变化。在本文中，我们提出了一种深度学习流程...

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head a...

前列腺癌是美国男性中最常见且第二致命的癌症。基于组织学图像的Gleason分级进行前列腺癌分类对于患者的风险评估和治疗规划至关重要。在此，我们展示了一种新的基于区域的卷积神经网络框架，用于使用上皮网络头进行多任务预测...

The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the ...

医学图像中标志物的识别和量化对于诊断、预后和疾病管理至关重要。监督机器学习能够检测和利用专家对训练样本进行标注后已知的先验发现。然而，监督学习的可扩展性较差，原因是所需训练样本的数量以及...的限制。

Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows, which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-...

光谱计算机断层扫描（CT）从多个窄能量窗口的投影中重建与材料相关的衰减图像，这对材料识别和分解具有重要意义。不幸的是，多能量投影数据集通常具有较低的信噪比（SNR）。最近，提出了一种空间-光谱立方体匹配框架（SSCMF）来探索非...

Ultrasound shear wave elastography is an imaging modality for noninvasive evaluation of tissue mechanical properties. However, many current techniques overestimate lesions dimension or shape especially when small inclusions are taken into account. In this paper, we propose a new method called local phase velocity-based imaging (LPVI) as an alternative technique to measure tissue elasticity. Two se...

超声剪切波弹性成像是一种无创评估组织力学特性的成像方式。然而，许多现有技术会高估病灶的尺寸或形状，尤其是当考虑小病变时。在本文中，我们提出了一种称为局部相速度成像（LPVI）的新方法，作为测量组织弹性的替代技术。两个...

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical resear...

影像引导通过让医生在MRI、CT、超声或其他相关图像中可视化活检针的针尖和轨迹相对于靶点的位置，提高了活检过程中组织取样的准确性。本文报告了一个在主动临床研究中开发和测试的用于MRI中快速自动针尖和轨迹定位与可视化的系统。

Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain’s dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states acr...

大脑活动是不同感觉反应的动态组合，因此大脑活动/状态随时间不断变化。然而，在任务fMRI数据中，对快速时间尺度上大脑动态功能状态的识别很少被探索。在本文中，我们提出了一种新颖的五层深度稀疏递归神经网络模型，以准确识别大脑状态...

For wrist complaints related to motion, a 2-D radiograph or CT scan of the static wrist may not always be considered diagnostic. 3-D motion imaging, i.e., multiple 3DCT scans in time (4DCT), enables quantifying carpal motion and comparing motion patterns of the affected wrist with those of the healthy contralateral side. The accuracy and precision of the method, however, is limited by noise and mo...

对于与运动相关的腕部不适，静态腕部的二维X光片或CT扫描可能并不总是具有诊断意义。三维运动成像，即随时间变化的多幅3DCT扫描（4DCT），能够量化腕骨运动并将患侧手腕的运动模式与健康对侧进行比较。然而，该方法的准确性和精确性受到噪声和...的限制。

The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metri...

描述不同脑区如何相互交互的问题在过去几年中引起了广泛关注。神经元群作为振荡器并通过同步进行通信的观点现在已被广泛接受。在这方面，EEG和MEG提供了允许在体内估计这种通信的信号。因此，基于相位的度量...

Due to peripheral nerve stimulation, the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger FoV, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is s...

由于外周神经刺激，磁粒子成像（MPI）方法在最大可施加激励场振幅方面受到限制。这进而导致覆盖的视野（FoV）大小被限制在几毫米。为了仍然捕获更大的FoV，MPI能够以多片方式快速采集体积。为此，小的激励体积被...

One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells usin...

在生物图像分析中，最重要且易出错的任务之一是分割接触或重叠的细胞。特别是在光学显微镜（包括透射光和共聚焦荧光显微镜）中，通常没有一致的判别信息来分离接触或重叠的细胞。期望将接触的前景像素分割成细胞...

The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cog...

出生后早期阶段经历了快速且动态的脑发育。然而，大脑解剖结构与认知能力之间的关系仍然未知。目前，文献中尚无明确的模型来表征这种关系。本文通过研究大脑皮层形态特征与认知能力之间的映射来探索这种关系。

We introduce a framework for the reconstruction of the amplitude, phase, and polarization of an optical vector-field using measurements acquired by an imaging device characterized by an integral transform with an unknown spatially variant kernel. By incorporating effective regularization terms, this new approach is able to recover an optical vector-field with respect to an arbitrary representation...

我们介绍了一种框架，利用由具有未知空间变化核的积分变换表征的成像设备获取的测量值，来重建光学矢量场的振幅、相位和极化。通过引入有效的正则化项，这种新方法能够恢复任意表示下的光学矢量场。

This paper describes the implementation of a novel and robust threshold energy calibration method for photon counting detectors using polychromatic X-ray tubes. Methods often used for such energy calibration may require re-orientation of the detector or introduce calibration errors that are flux and acquisition time-dependent. Our newly proposed “differential intensity ratios” (DIR) method offers ...

本文描述了一种使用多色X射线管对光子计数探测器进行稳健的阈值能量标定新方法。常用的能量标定方法可能需要重新调整探测器方向，或者引入依赖于通量和采集时间的标定误差。我们新提出的“差分强度比”（DIR）方法提供了……

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in echo planar imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time. Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The ...

我们提出了一种结构化低秩算法，用于无校准地补偿回波平面成像（EPI）MRI数据中的场不均匀性伪影。我们使用两个回波时间不同的EPI读出序列来采集数据。通过时间分割，我们将场不均匀性补偿问题重新表述为从高度欠采样的傅立叶测量中恢复图像时间序列。

Identification and treatment of obstructive airway disorders (OADs) are greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by the reduction in luminal cr...

通过气道管腔几何结构的成像，极大有助于阻塞性气道疾病（OADs）的识别和治疗。解剖光学相干断层扫描（aOCT）是一种有前途的高速、微创内镜成像方式，可提供上气道微米级分辨率扫描。OADs中气流阻力直接由管腔缩小引起...

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