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
Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the s...Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.展开更多
Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep ...Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.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.
文摘Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
文摘Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.
