ResUNet

  • 文章类型: Journal Article
    阿尔茨海默病(AD)是一种退行性神经系统疾病,可导致多种大脑过程的恶化(例如记忆丧失)。AD中最显著的物理变化是脑细胞的损伤。大脑图像的准确检查可能有助于更早地发现疾病,因为早期诊断对于提高患者护理和治疗效果至关重要。因此,一个简单而无错误的AD诊断系统最近受到了很多研究的关注。传统的图像处理技术有时无法观察到重要的特征。因此,本研究的目的是开发一种使用磁共振成像(MRI)识别AD的准确有效的方法。首先,使用强大的基于DeepResUnet的方法对MRI图像中的大脑区域进行分割。然后,使用基于多尺度注意连体网络(MASNet)的网络来恢复来自分割图像的全局和局部特征。提取特征后,基于煤泥模具算法的特征选择过程。最后,使用EfficientNetB7模型对AD的阶段进行分类。使用Kaggle数据集和AD神经成像计划(ADNI)数据集的脑部MRI扫描测试了所提出方法的有效性,它达到99.31%和99.38%的准确度,分别。最后,研究结果表明,该方法有助于AD的准确分类。由RamaswamyH.Sarma沟通。
    Alzheimer\'s disease (AD) is a degenerative neurologic condition that results in the deterioration of several brain processes (e.g. memory loss). The most notable physical alteration in AD is the impairment of brain cells. An accurate examination of brain pictures may help to find the disease earlier because early diagnosis is crucial to enhancing patient care and treatment outcomes. Therefore, an easy and error-free system for AD diagnosis has recently received much research attention. Conventional image processing techniques sometimes cannot observe the significant features. As a result, the objective of this research is to develop an accurate and efficient method for identifying AD using magnetic resonance imaging (MRI). To begin with, the brain regions in the MRI images are segmented using a powerful Deep ResUnet-based approach. Then, the global and local features from the segmented images are recovered using a Multi-Scale Attention Siamese Network (MASNet)-based network. After extracting the features, the Slime Mould Algorithm-based feature selection process is conducted. Finally, the stages of AD are categorized using the EfficientNetB7 model. The efficacy of the presented method has been tested using brain MRI scans from the Kaggle dataset and the AD Neuroimaging Initiative (ADNI) dataset, and it achieves 99.31% and 99.38% accuracy, respectively. Finally, the study results show that the suggested method is helpful for accurate AD categorization.Communicated by Ramaswamy H. Sarma.
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  • 文章类型: Journal Article
    睾丸体积(TV)是监测睾丸功能和病理的必要参数。然而,电流测量工具,包括睾丸测定仪和超声检查,在获得准确和个性化的电视测量时遇到挑战。
    基于磁共振成像(MRI),这项研究旨在建立一个深度学习模型,并评估其在分割睾丸和测量电视方面的功效。
    研究队列包括回顾性收集的患者数据(N=200)和前瞻性收集的数据集,包括10名健康志愿者。回顾性数据集分为训练集和独立验证集,8:2随机分布。10名健康志愿者中的每一个经历5次扫描(形成测试数据集)以评估测量再现性。应用ResUNet算法对测试进行分段。通过将体素体积乘以体素的数量来计算每个睾丸的体积。专家手动确定的面具被用作评估深度学习模型性能的基础事实。
    深度学习模型在验证队列中的平均Dice评分为0.926±0.034(左睾丸为0.921±0.026,右睾丸为0.926±0.034),在测试队列中的平均Dice评分为0.922±0.02(左睾丸为0.931±0.019,右睾丸为0.932±0.022)。手动电视和自动电视之间存在很强的相关性(验证队列中的R2范围为0.974至0.987;测试队列中的R2范围为0.936至0.973)。在验证队列中,手动和自动测量之间的体积差异为0.838±0.991(LTV为0.209±0.665,RTV为0.630±0.728),在测试队列中为0.815±0.824(LTV为0.303±0.664,RTV为0.511±0.444)。此外,深度学习模型在确定TV时表现出优异的可重复性(组内相关性>0.9).
    基于MRI的深度学习模型是测量电视的准确可靠的工具。
    UNASSIGNED: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.
    UNASSIGNED: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.
    UNASSIGNED: The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.
    UNASSIGNED: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV.
    UNASSIGNED: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.
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  • 文章类型: Journal Article
    背景:诊断和治疗计划在提高肿瘤患者的生存率中起着非常重要的作用。然而,形状有很高的可变性,尺寸,和肿瘤的结构,使自动分割变得困难。本文提出了一种自动准确的脑肿瘤检测和分割方法。
    方法:使用改良的ResNet50模型进行肿瘤检测,本文提出了一种基于ResUNetmodel的卷积神经网络分割方法。在相同的数据集上进行检测和分割,FLAIR,以及从癌症影像档案中收集的110例患者的造影后MRI图像。由于使用了残差网络,作者观察到评估参数的改善,例如肿瘤检测的准确性和肿瘤分割的骰子相似系数。
    结果:通过分割模型实现的肿瘤检测和Dice相似系数的准确率分别为96.77%和0.893,对于TCIA数据集。基于手动分割和现有分割技术对结果进行了比较。还使用SSIM值将肿瘤掩模与地面实况进行了单独比较。所提出的检测和分割模型在BraTS2015和BraTS2017数据集上进行了验证,结果是共识。
    结论:在检测和分割模型中使用残差网络可提高准确性和DSC评分。与UNet模型相比,DSC评分提高了5.9%,模型的准确度从92%提高到96.77%。
    Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for Brain tumors are proposed in this paper.
    A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
    The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.
    The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
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  • 文章类型: Journal Article
    Bosniak肾囊肿分类已广泛用于确定肾囊肿的复杂性。然而,事实证明,大约一半的接受波斯尼亚III类手术的患者,承担手术风险,奖励他们根本没有临床益处。这是因为它们的病理结果显示囊肿实际上是良性的而不是恶性的。这个问题激励我们使用最近流行的深度学习技术,并研究替代分析方法,以对计算机断层扫描(CT)图像进行精确的二进制分类(良性或恶性肿瘤)。为了实现我们的目标,需要两个连续的步骤-从CT图像中分割肾脏器官或病变,然后对分割的肾脏进行分类。在本文中,我们建议使用2.5DResUNet和2.5DDenseUNet进行肾脏分割研究,以有效提取切片内和切片间特征.我们的模型在两种不同的训练环境中的肾脏肿瘤分割(KiTS19)挑战的公共数据集上进行了训练和验证。因此,在由60例患者组成的KiTS19验证组中,所有实验模型均获得至少95%的高平均肾Dice评分.除了KiTS19数据集,我们还对四名泰国患者的腹部CT图像进行了单独的实验。根据四名泰国患者,我们的实验模型显示性能下降,其中肾脏骰子的最佳平均得分为87.60%。
    Bosniak renal cyst classification has been widely used in determining the complexity of a renal cyst. However, it turns out that about half of patients undergoing surgery for Bosniak category III, take surgical risks that reward them with no clinical benefit at all. This is because their pathological results reveal that the cysts are actually benign not malignant. This problem inspires us to use recently popular deep learning techniques and study alternative analytics methods for precise binary classification (benign or malignant tumor) on Computerized Tomography (CT) images. To achieve our goal, two consecutive steps are required-segmenting kidney organs or lesions from CT images then classifying the segmented kidneys. In this paper, we propose a study of kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for efficiently extracting intra-slice and inter-slice features. Our models are trained and validated on the public data set from Kidney Tumor Segmentation (KiTS19) challenge in two different training environments. As a result, all experimental models achieve high mean kidney Dice scores of at least 95% on the KiTS19 validation set consisting of 60 patients. Apart from the KiTS19 data set, we also conduct separate experiments on abdomen CT images of four Thai patients. Based on the four Thai patients, our experimental models show a drop in performance, where the best mean kidney Dice score is 87.60%.
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  • 文章类型: Journal Article
    被认为是被忽视的热带病理学,查加斯病每年造成数千人死亡,它是由寄生虫克氏锥虫引起的。由于许多感染者可以保持无症状,快速诊断对于适当的干预是必要的。血液样本中的寄生虫显微镜观察是诊断查加斯病初期的金标准方法;然而,这是一个耗时的过程,需要专家干预,,并且目前没有有效的方法来自动执行此任务。因此,我们提出了一种有效的残差卷积神经网络,名为Res2Unet,为了对克氏锥虫寄生虫进行语义分割,具有主动轮廓损失和改进的剩余连接,其设计基于Heun的常微分方程求解方法。该模型在626个血液样本图像的数据集上进行训练,并在207个图像的数据集上进行测试。验证实验报告说,我们的模型实现了Dice系数得分为0.84,精度值为0.85,召回值为0.82,优于当前最先进的方法。由于恰加斯病是一种严重而无声的疾病,我们的计算模型可能有利于卫生保健提供者对这一世界性影响做出及时诊断.
    Considered a neglected tropical pathology, Chagas disease is responsible for thousands of deaths per year and it is caused by the parasite Trypanosoma cruzi. Since many infected people can remain asymptomatic, a fast diagnosis is necessary for proper intervention. Parasite microscopic observation in blood samples is the gold standard method to diagnose Chagas disease in its initial phase; however, this is a time-consuming procedure, requires expert intervention, and there is currently no efficient method to automatically perform this task. Therefore, we propose an efficient residual convolutional neural network, named Res2Unet, to perform a semantic segmentation of Trypanosoma cruzi parasites, with an active contour loss and improved residual connections, whose design is based on Heun\'s method for solving ordinary differential equations. The model was trained on a dataset of 626 blood sample images and tested on a dataset of 207 images. Validation experiments report that our model achieved a Dice coefficient score of 0.84, a precision value of 0.85, and a recall value of 0.82, outperforming current state-of-the-art methods. Since Chagas disease is a severe and silent illness, our computational model may benefit health care providers to give a prompt diagnose for this worldwide affection.
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  • 文章类型: Journal Article
    We present an automated method for measuring the sagittal vertical axis (SVA) from lateral radiography of whole spine using a convolutional neural network for keypoint detection (ResUNet) with our improved localization method. The algorithm is robust to various clinical conditions, such as degenerative changes or deformities. The ResUNet was trained and evaluated on 990 standing lateral radiographs taken at Chang Gung Memorial Hospital, Linkou and performs SVA measurement with median absolute error of 1.183 ± 0.166 mm. The 5-mm detection rate of the C7 body and the sacrum are 91% and 87%, respectively. The SVA calculation takes approximately 0.2 s per image. The intra-class correlation coefficient of the SVA estimates between the algorithm and physicians of different years of experience ranges from 0.946 to 0.993, indicating an excellent consistency. The superior performance of the proposed method and its high consistency with physicians proved its usefulness for automatic measurement of SVA in clinical settings.
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