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Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks 使用卷积神经网络从x射线图像中自动检测COVID-19的迁移学习
IF 7.6 Q1 Medicine Pub Date : 2020-08-31 DOI: 10.1101/2020.08.25.20182170
Mundher Mohammed Taresh, N. Zhu, T. Ali, Asaad Shakir Hameed, Modhi Lafta Mutar
Novel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID- 19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images. Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was derived from several open sources of X-Rays, and the data available online. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PYCM was used to support the statistical parameters. The study revealed the superiority of Model vgg16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep Learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.
新型冠状病毒肺炎(新冠肺炎)是一种传染性疾病,已导致全球数千人死亡,数百万人感染。因此,所有能够高精度快速检测COVID-19感染的技术设备都可以为医疗保健专业人员提供帮助。本研究旨在探索人工智能(AI)在基于胸部X射线成像的新冠肺炎快速可靠检测中的有效性。在这项研究中,应用可靠的预先训练的深度学习算法,实现了从数字胸部X射线图像中自动检测COVID-19诱导的肺炎。此外,该研究旨在评估近年来为医学图像分类提出的先进神经结构的性能。实验中使用的数据集涉及274例新冠肺炎病例、380例病毒性肺炎病例和380例健康病例,这些数据来源于几个开放的X射线来源和在线数据。混淆矩阵为测试后分类模型提供了基础。此外,还使用了一个开源的PYCM库来支持统计参数。该研究揭示了vgg16模型与用于进行这项研究的其他模型相比的优势,在这些模型中,该模型在总分和基于班级的分数方面表现最好。根据研究结果,X射线成像的深度学习可用于收集与新冠肺炎感染相关的关键生物标志物。该技术有助于医生对新冠肺炎感染进行诊断。同时,这种计算机辅助诊断工具的高精度可以显著提高新冠肺炎诊断的速度和准确性。
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引用次数: 72
An Algorithm of l 1-Norm and l 0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection. 有限投影CT图像重构的1- 1范数和1- 0范数正则化算法。
IF 7.6 Q1 Medicine Pub Date : 2020-08-28 eCollection Date: 2020-01-01 DOI: 10.1155/2020/8873865
Xiezhang Li, Guocan Feng, Jiehua Zhu

The l 1-norm regularization has attracted attention for image reconstruction in computed tomography. The l 0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l 1-norm and l 0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l 0-norm regularization.

1.1范数正则化在计算机断层扫描图像重建中受到了广泛的关注。图像梯度的0范数提供了图像梯度稀疏度的度量。本文提出了一种新的l - 1范数和l - 0范数组合正则化模型,用于计算机断层扫描中有限投影数据的图像重建。在代数框架下,提出了一种采用硬阈值法的非单调交替方向算法来有效解决优化问题的算法。数值实验表明,通过引入0范数正则化,该算法有了很大的改进。
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引用次数: 2
COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings. 使用公开可用的放射科医生评审的胸部x射线图像作为训练数据的COVID-19深度学习预测模型:初步发现。
IF 7.6 Q1 Medicine Pub Date : 2020-08-18 eCollection Date: 2020-01-01 DOI: 10.1155/2020/8828855
Mohd Zulfaezal Che Azemin, Radhiana Hassan, Mohd Izzuddin Mohd Tamrin, Mohd Adli Md Ali

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

深度学习研究的关键部分是训练数据集的可用性。由于公开可用的COVID-19胸部x线图像数量有限,基于这些图像开发的深度学习模型检测COVID-19病例的泛化和鲁棒性值得怀疑。我们的目标是使用数千张现成的具有与COVID-19相关临床表现的胸片图像作为训练数据集,与已确诊的COVID-19病例的图像相互排斥,这些图像将用作测试数据集。我们使用了一个基于ResNet-101卷积神经网络架构的深度学习模型,该模型经过预训练,可以从一百万张图像中识别物体,然后再进行重新训练,以检测胸部x射线图像中的异常。该模型在受试者工作曲线下面积、灵敏度、特异度和准确度方面的表现分别为0.82、77.3%、71.8%和71.9%。本研究的优势在于使用与COVID-19病例具有强烈临床相关性的标签,并使用相互排斥的公开数据进行培训、验证和测试。
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引用次数: 96
Comparison of Low-Pass Filters for SPECT Imaging. SPECT成像低通滤波器的比较。
IF 7.6 Q1 Medicine Pub Date : 2020-04-01 eCollection Date: 2020-01-01 DOI: 10.1155/2020/9239753
Inayatullah S Sayed, Siti S Ismail

In single photon emission computed tomography (SPECT) imaging, the choice of a suitable filter and its parameters for noise reduction purposes is a big challenge. Adverse effects on image quality arise if an improper filter is selected. Filtered back projection (FBP) is the most popular technique for image reconstruction in SPECT. With this technique, different types of reconstruction filters are used, such as the Butterworth and the Hamming. In this study, the effects on the quality of reconstructed images of the Butterworth filter were compared with the ones of the Hamming filter. A Philips ADAC forte gamma camera was used. A low-energy, high-resolution collimator was installed on the gamma camera. SPECT data were acquired by scanning a phantom with an insert composed of hot and cold regions. A Technetium-99m radioactive solution was homogenously mixed into the phantom. Furthermore, a symmetrical energy window (20%) centered at 140 keV was adjusted. Images were reconstructed by the FBP method. Various cutoff frequency values, namely, 0.35, 0.40, 0.45, and 0.50 cycles/cm, were selected for both filters, whereas for the Butterworth filter, the order was set at 7. Images of hot and cold regions were analyzed in terms of detectability, contrast, and signal-to-noise ratio (SNR). The findings of our study indicate that the Butterworth filter was able to expose more hot and cold regions in reconstructed images. In addition, higher contrast values were recorded, as compared to the Hamming filter. However, with the Butterworth filter, the decrease in SNR for both types of regions with the increase in cutoff frequency as compared to the Hamming filter was obtained. Overall, the Butterworth filter under investigation provided superior results than the Hamming filter. Effects of both filters on the quality of hot and cold region images varied with the change in cutoff frequency.

在单光子发射计算机断层扫描(SPECT)成像中,选择合适的滤波器及其参数以达到降噪目的是一个很大的挑战。如果选择不合适的滤镜,会对图像质量产生不利影响。滤波反投影(FBP)是SPECT中最常用的图像重建技术。利用这种技术,使用不同类型的重建滤波器,如巴特沃斯和汉明。本研究比较了巴特沃斯滤波器与汉明滤波器对重建图像质量的影响。使用飞利浦ADAC强光伽马相机。在伽马照相机上安装了一个低能量、高分辨率的准直器。SPECT数据是通过扫描一个由冷热区组成的插入体来获得的。将锝-99m放射性溶液均匀地混合到幻影中。此外,调节了以140 keV为中心的对称能量窗(20%)。采用FBP方法重建图像。不同的截止频率值,即0.35,0.40,0.45和0.50周期/厘米,被选择为两个滤波器,而巴特沃斯滤波器,顺序设置为7。根据可检测性、对比度和信噪比(SNR)对冷热地区的图像进行分析。我们的研究结果表明,巴特沃斯滤波器能够在重建图像中暴露更多的冷热区域。此外,与汉明滤波器相比,记录了更高的对比度值。然而,与汉明滤波器相比,使用巴特沃斯滤波器,两种类型的区域的信噪比都随着截止频率的增加而降低。总的来说,所研究的巴特沃斯滤波器比汉明滤波器提供了更好的结果。两种滤波器对冷热区图像质量的影响随截止频率的变化而变化。
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引用次数: 3
Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset. 基于大规模手部x射线数据集的全自动骨龄评估。
IF 7.6 Q1 Medicine Pub Date : 2020-03-03 eCollection Date: 2020-01-01 DOI: 10.1155/2020/8460493
Xiaoying Pan, Yizhe Zhao, Hao Chen, De Wei, Chen Zhao, Zhi Wei

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance.

骨龄评估(BAA)是评估儿童生物学成熟度的重要课题。由于手工方法耗时长,且易受观测者变化的影响,因此开发BAA的计算机辅助和自动化方法是很有吸引力的。本文提出了一种全自动BAA方法。为了消除原始x射线图像中的噪声,我们首先使用U-Net从原始x射线图像中精确分割手掩膜图像。尽管U-Net可以实现高精度的分割,但它需要更大的标注数据集。为了减轻标注负担,我们建议使用深度主动学习(deep active learning, AL)来有意地选择具有足够信息的未标记数据样本。这些示例提供给Oracle进行注释。之后,它们被用于后续的训练。最初,只有300张数据需要手工标注,然后在人工智能框架下改进的U-Net可以鲁棒分割RSNA数据集中的所有12611张图像。人工智能分割模型在标注测试集中的Dice得分为0.95。为了优化学习过程,我们在ImageNet上使用了六个具有预训练权值的现成深度卷积神经网络(cnn)。我们使用迁移学习技术提取预处理手图像的特征。最后,应用了多种集成回归算法来执行BAA。此外,我们选择一个特定的CNN来提取特征,并解释为什么我们选择该CNN。实验结果表明,该方法在RSNA数据集上实现了男性和女性队列的人工骨龄和预测骨龄分别约为6.96个月和7.35个月的差异。这些精度可与最先进的性能相媲美。
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引用次数: 25
Microvascular Ultrasonic Imaging of Angiogenesis Identifies Tumors in a Murine Spontaneous Breast Cancer Model. 血管生成的微血管超声成像识别小鼠自发性乳腺癌模型中的肿瘤。
IF 7.6 Q1 Medicine Pub Date : 2020-02-06 eCollection Date: 2020-01-01 DOI: 10.1155/2020/7862089
Sarah E Shelton, Jodi Stone, Fei Gao, Donglin Zeng, Paul A Dayton

The purpose of this study is to determine if microvascular tortuosity can be used as an imaging biomarker for the presence of tumor-associated angiogenesis and if imaging this biomarker can be used as a specific and sensitive method of locating solid tumors. Acoustic angiography, an ultrasound-based microvascular imaging technology, was used to visualize angiogenesis development of a spontaneous mouse model of breast cancer (n = 48). A reader study was used to assess visual discrimination between image types, and quantitative methods utilized metrics of tortuosity and spatial clustering for tumor detection. The reader study resulted in an area under the curve of 0.8, while the clustering approach resulted in the best classification with an area under the curve of 0.95. Both the qualitative and quantitative methods produced a correlation between sensitivity and tumor diameter. Imaging of vascular geometry with acoustic angiography provides a robust method for discriminating between tumor and healthy tissue in a mouse model of breast cancer. Multiple methods of analysis have been presented for a wide range of tumor sizes. Application of these techniques to clinical imaging could improve breast cancer diagnosis, as well as improve specificity in assessing cancer in other tissues. The clustering approach may be beneficial for other types of morphological analysis beyond vascular ultrasound images.

本研究的目的是确定微血管扭曲是否可以作为肿瘤相关血管生成的成像生物标志物,以及这种生物标志物的成像是否可以作为定位实体肿瘤的一种特异性和敏感性方法。声学血管造影是一种基于超声的微血管成像技术,用于观察自发性乳腺癌小鼠模型(n = 48)的血管生成发育。一项读者研究用于评估图像类型之间的视觉区分,定量方法利用扭曲度和空间聚类指标进行肿瘤检测。读者研究的曲线下面积为0.8,聚类方法的最佳分类曲线下面积为0.95。定性和定量方法均得出敏感性与肿瘤直径的相关性。超声血管造影血管几何成像提供了一个强大的方法来区分肿瘤和健康组织的小鼠乳腺癌模型。多种分析方法已经提出了广泛的肿瘤大小范围。将这些技术应用于临床影像学,可以提高乳腺癌的诊断,也可以提高其他组织中癌症评估的特异性。聚类方法可能有利于血管超声图像以外的其他类型的形态学分析。
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引用次数: 6
Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting. 基于Patch-Based Processing、k-means聚类和目标计数混合技术的早期多发性脑肿瘤检测与定位。
IF 7.6 Q1 Medicine Pub Date : 2020-01-06 eCollection Date: 2020-01-01 DOI: 10.1155/2020/9035096
Mohamed Nasor, Walid Obaid

Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

脑肿瘤是影响许多人生活的主要健康问题。这些肿瘤分为良性和癌性。如果诊断和治疗不当,后者可能是致命的。因此,在脑肿瘤发展的早期阶段进行诊断,可以显著提高患者治疗后完全康复的机会。除了实验室分析外,临床医生和外科医生还从磁共振成像(MRI)、x射线和计算机断层扫描(CT)等各种系统记录的医学图像中提取信息。提取的信息用于识别脑肿瘤的基本特征(位置、大小和类型),以实现准确的诊断,确定最合适的治疗方案。在本文中,我们提出了一种自动机器视觉技术,用于在MRI图像的早期阶段检测和定位脑肿瘤,该技术结合了k-means聚类、基于补丁的图像处理、目标计数和肿瘤评估。该技术在20张真实的MRI图像上进行了测试,发现能够检测MRI图像中的多个肿瘤,无论其强度水平变化,大小和位置如何,包括那些非常小的肿瘤。除了用于诊断之外,该技术还可以集成到自动化治疗仪器和机器人手术系统中。
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引用次数: 24
Corrigendum to “Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery” “肿瘤切除手术中脑转移的术中成像方式和补偿”的勘误表
IF 7.6 Q1 Medicine Pub Date : 2019-10-01 DOI: 10.1155/2019/9249016
Siming Bayer, A. Maier, M. Ostermeier, R. Fahrig
[This corrects the article DOI: 10.1155/2017/6028645.].
[这更正了文章DOI: 10.1155/2017/6028645]。
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引用次数: 0
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems 交互式图像分割系统的半自动化可用性评估框架
IF 7.6 Q1 Medicine Pub Date : 2019-09-01 DOI: 10.1155/2019/1464592
Mario Amrehn, S. Steidl, Reinier Kortekaas, Maddalena Strumia, M. Weingarten, M. Kowarschik, A. Maier
For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.
对于复杂的分割任务,完全自动化系统的可实现精度是固有的有限的。具体来说,当需要对少量给定数据集进行精确分割时,半自动方法对用户有明显的好处。人机交互优化是交互式图像分割的重要组成部分。然而,介绍新型交互式分割系统(ISS)的出版物往往缺乏对HCI方面的客观比较。研究表明,即使整个交互式原型的底层分割算法相同,其用户体验也可能有很大差异。因此,用户更喜欢简单的界面以及相当大的自由度来控制分割的每个迭代步骤。本文在广泛的用户研究的基础上,提出了一种客观的ISS比较方法。总结性的定性内容分析是通过抽象参与者给出的视觉和言语反馈来进行的。用户通过系统可用性量表(SUS)和AttrakDiff-2问卷对分割系统进行直接评估。此外,还介绍了这些研究中关于可用性方面的发现的近似值,仅从系统可测量的用户在使用交互式分割原型期间的行为中进行。所有问卷结果的预测平均相对误差为8.9%,接近问卷结果本身的预期精度。这种自动化评估方案可以显著减少调查原型的用户界面(UI)特征和分割方法的每个变化所需的资源。
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引用次数: 6
Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction 使用图像强度不均匀性校正的非对比头部CT急性梗死体积的自动估计
IF 7.6 Q1 Medicine Pub Date : 2019-08-21 DOI: 10.1155/2019/1720270
K. Cauley, G. Mongelluzzo, S. Fielden
Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
在中风发作的最初几个小时内进行的非光栅头部CT扫描中,识别早期缺血性变化(EIC)可能对后续治疗具有重要意义,尽管这些研究对早期中风的界定很差。早期梗死缺乏清晰的病变边界,无法进行手动体积测量,也无法使用边缘检测或区域填充算法进行测量。我们希望检验这样一种假设,即图像强度不均匀性校正可以为识别早期缺血性梗死特有的细微区域低密度提供一种灵敏的方法。利用图像强度不均匀性校正(IIC)和强度阈值技术,开发了一种数字图像分析算法。比较了两种不同的IIC算法(FSL和ITK)。该方法通过模拟梗死和临床病例进行评估。对于合成梗死,测量的梗死体积与真实病变体积具有很强的相关性(对于密度降低20%的“梗死”,两种算法的Pearson r=0.998);两种算法都显示出随着病变大小的增加和病变密度的降低而提高的准确性。在临床病例中(30例患者中有41例急性梗死),使用FSL IIC计算的梗死体积与ASPECTS评分(Pearson r=0.680)和入院NIHSS(Pearsonr=0.544)相关。计算的梗死容量与静脉注射tPA治疗的临床决定高度相关。当应用于非光栅头CT时,图像强度不均匀性校正提供了一种用于图像分析的工具,以帮助检测EIC,并评估和指导扫描质量的改进,以实现EIC的最佳检测。
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引用次数: 9
期刊
International Journal of Biomedical Imaging
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