为使桥梁病害检测更加高效、客观和智能,提出一种自动识别并定量计算混凝土病害尺寸的方法。该方法采用视觉几何组网络(Visual Geometry Group Network,VGG)作为U形网络(U-Net)的主干网络,对混凝土病害(剥落、裂缝和露筋)图像进行语义分...为使桥梁病害检测更加高效、客观和智能,提出一种自动识别并定量计算混凝土病害尺寸的方法。该方法采用视觉几何组网络(Visual Geometry Group Network,VGG)作为U形网络(U-Net)的主干网络,对混凝土病害(剥落、裂缝和露筋)图像进行语义分割,采用数学形态学算法对图像中的病害区域进行优化。通过MATLAB软件计算得到优化后的分割图像中病害区域像素点的数量,并利用参照物标定出图像中单个像素点的尺寸,计算得到混凝土病害的面积(或长度)。采用该方法对河南省许昌市17座现役钢筋混凝土桥梁病害图像进行语义分割实验。结果表明:U-Net能以较高的精度对复杂背景下混凝土桥梁多类病害进行像素级的分类,类别平均像素准确率为90.53%,平均交并比为80.54%。使用数学形态学对语义分割图像进行优化后,计算精度明显提高,优化后的误差绝对值为0.08%~0.21%。展开更多
肝脏以及肝脏肿瘤的有效分割是肝部疾病在临床诊断的关键步骤。文章针对肝脏结构复杂、肝脏与相邻器官像素强度差异小、肝脏边界模糊等特点,提出了一种可以进行多尺度特征融合的肝脏肿瘤分割网络。该方法根据肝脏CT图像特点,在3D U-Net...肝脏以及肝脏肿瘤的有效分割是肝部疾病在临床诊断的关键步骤。文章针对肝脏结构复杂、肝脏与相邻器官像素强度差异小、肝脏边界模糊等特点,提出了一种可以进行多尺度特征融合的肝脏肿瘤分割网络。该方法根据肝脏CT图像特点,在3D U-Net的基础上进行改进,提升了网络提取特征的感受野,减少了传递过程中信息的丢失。同时,在网络中引入密集融合模块,该模块可对不同尺度下的特征图进行特征融合,通过边缘信息和差异信息的融合来提升网络信息提取的性能,避免传递过程中肿瘤部分等小目标特征的丢失。在LiTS17数据集上的实验结果表明,该模型对肝脏分割的Dice系数达到了0.9504,对肿瘤分割的Dice系数达到了0.7046,实验结果证明了该方法的出色分割性能和有效性。Effective segmentation of the liver and liver tumors is a key step in the clinical diagnosis of liver diseases. This paper addresses the complexity of liver structure, the small difference in pixel intensity between the liver and adjacent organs, and the vagueness of liver boundaries, proposing a liver tumor segmentation network capable of multi-scale feature fusion. Based on the characteristics of liver CT images, this method improves upon the 3D U-Net, enhancing the network’s receptive field for feature extraction and reducing information loss during transmission. At the same time, a dense fusion module is introduced into the network, which can fuse feature maps at different scales, enhancing the network’s performance in information extraction through the integration of edge and difference information and preventing the loss of small target features such as tumor parts during transmission. Experimental results on the LiTS17 dataset show that the model achieved a Dice coefficient of 0.9504 for liver segmentation and 0.7046 for tumor segmentation, demonstrating the excellent segmentation performance and effectiveness of this method.展开更多
文摘为使桥梁病害检测更加高效、客观和智能,提出一种自动识别并定量计算混凝土病害尺寸的方法。该方法采用视觉几何组网络(Visual Geometry Group Network,VGG)作为U形网络(U-Net)的主干网络,对混凝土病害(剥落、裂缝和露筋)图像进行语义分割,采用数学形态学算法对图像中的病害区域进行优化。通过MATLAB软件计算得到优化后的分割图像中病害区域像素点的数量,并利用参照物标定出图像中单个像素点的尺寸,计算得到混凝土病害的面积(或长度)。采用该方法对河南省许昌市17座现役钢筋混凝土桥梁病害图像进行语义分割实验。结果表明:U-Net能以较高的精度对复杂背景下混凝土桥梁多类病害进行像素级的分类,类别平均像素准确率为90.53%,平均交并比为80.54%。使用数学形态学对语义分割图像进行优化后,计算精度明显提高,优化后的误差绝对值为0.08%~0.21%。
文摘肝脏以及肝脏肿瘤的有效分割是肝部疾病在临床诊断的关键步骤。文章针对肝脏结构复杂、肝脏与相邻器官像素强度差异小、肝脏边界模糊等特点,提出了一种可以进行多尺度特征融合的肝脏肿瘤分割网络。该方法根据肝脏CT图像特点,在3D U-Net的基础上进行改进,提升了网络提取特征的感受野,减少了传递过程中信息的丢失。同时,在网络中引入密集融合模块,该模块可对不同尺度下的特征图进行特征融合,通过边缘信息和差异信息的融合来提升网络信息提取的性能,避免传递过程中肿瘤部分等小目标特征的丢失。在LiTS17数据集上的实验结果表明,该模型对肝脏分割的Dice系数达到了0.9504,对肿瘤分割的Dice系数达到了0.7046,实验结果证明了该方法的出色分割性能和有效性。Effective segmentation of the liver and liver tumors is a key step in the clinical diagnosis of liver diseases. This paper addresses the complexity of liver structure, the small difference in pixel intensity between the liver and adjacent organs, and the vagueness of liver boundaries, proposing a liver tumor segmentation network capable of multi-scale feature fusion. Based on the characteristics of liver CT images, this method improves upon the 3D U-Net, enhancing the network’s receptive field for feature extraction and reducing information loss during transmission. At the same time, a dense fusion module is introduced into the network, which can fuse feature maps at different scales, enhancing the network’s performance in information extraction through the integration of edge and difference information and preventing the loss of small target features such as tumor parts during transmission. Experimental results on the LiTS17 dataset show that the model achieved a Dice coefficient of 0.9504 for liver segmentation and 0.7046 for tumor segmentation, demonstrating the excellent segmentation performance and effectiveness of this method.
