为提取人体肝脏CT图像中的肿瘤区域,提出一种基于动态自适应区域生长的算法进行肿瘤分割.通过自适应区域生长算法对CT图像进行预分割,得到感兴趣区域(region of interest,ROI),利用数学形态学滤波填充ROI中的空洞区域,最终提取肿瘤区域...为提取人体肝脏CT图像中的肿瘤区域,提出一种基于动态自适应区域生长的算法进行肿瘤分割.通过自适应区域生长算法对CT图像进行预分割,得到感兴趣区域(region of interest,ROI),利用数学形态学滤波填充ROI中的空洞区域,最终提取肿瘤区域.通过对多组病人的CT图像进行实验,结果显示该算法对肝脏肿瘤的分割效果良好.展开更多
BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algor...BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liv展开更多
目的肝纤维化是众多慢性肝脏疾病的常见表现,如不及时治疗可发展为肝硬化甚至引发肝癌。肝纤维化的准确评估对临床治疗和预后评估等至关重要。目前,肝纤维化的诊断通过肝穿活检判断,有创且有并发症危险。为此,基于影像学的无创诊断方法...目的肝纤维化是众多慢性肝脏疾病的常见表现,如不及时治疗可发展为肝硬化甚至引发肝癌。肝纤维化的准确评估对临床治疗和预后评估等至关重要。目前,肝纤维化的诊断通过肝穿活检判断,有创且有并发症危险。为此,基于影像学的无创诊断方法越来越受到关注。本文提出一种基于通道注意力与空间注意力机制改进的用于肝纤维化区域的自动化分割U-Net(liver fibrosis region segmentation network based on spatial and channel attention mechanisms, LFSCA-UNet)。方法依据Attention U-Net的改进方式,围绕U-Net的跳跃连接结构进行基于注意力的改进,在AG(attention gate)的基础上,加入以ECA(efficient channel attention)模块为实现方式的通道注意力机制,依据加入ECA的位置,LFSCA-UNet分为A、B、C共3个子型。结果在肝数据集上与其他实验网络进行评估对比,本文提出的LFSCA-UNet网络结构平均Dice系数达到了93.33%,相比原始U-Net的Dice系数提高了0.539 6%。结论本文方法将空间注意力机制与通道注意力机制进行结合,有效提高了肝纤维化区域的分割精度,对空间注意力模块使用通道注意力模块优化输入和输出,增加了网络的稳定性,提升了网络的整体效果。展开更多
Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present wi...Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.展开更多
显式模型下建立的肝脏分割算法受适用条件的限制,不能有效地控制分割进程,制约了算法鲁棒性和泛化能力的提高.针对这一问题,该文提出了一个新的方法,将肝脏分割问题转化为隐式函数最优值搜索,依据图像实际数据和先验形状信息有效地控制...显式模型下建立的肝脏分割算法受适用条件的限制,不能有效地控制分割进程,制约了算法鲁棒性和泛化能力的提高.针对这一问题,该文提出了一个新的方法,将肝脏分割问题转化为隐式函数最优值搜索,依据图像实际数据和先验形状信息有效地控制分割进程,以迭代搜索策略得到肝脏的最优分割结果.每一个迭代步骤由两部分组成:首先,利用MRF(Markov Random Field)实现已有肝脏轮廓邻域的局部再分割,重新调整肝脏轮廓;然后,利用先验形状稀疏表示调整后的形状,有效地修正噪声、边界模糊等因素引起的错误分割,并将修正后的肝脏轮廓用于下一轮局部再分割.肝脏分割结果在迭代过程中不断逼近真实值.为了提高形状稀疏表示的计算精度和速度,该文改进了相关技术细节,提出了动态字典生成策略,并利用混合粒子群算法求解稀疏表示方程.与金标准相比,该文所提算法在边界模糊、对比度低、存在大量其他干扰组织区域等不利因素的情景中,其分割精度(Volume Overlap Percentage,VOE)可达到90%以上.展开更多
文摘为提取人体肝脏CT图像中的肿瘤区域,提出一种基于动态自适应区域生长的算法进行肿瘤分割.通过自适应区域生长算法对CT图像进行预分割,得到感兴趣区域(region of interest,ROI),利用数学形态学滤波填充ROI中的空洞区域,最终提取肿瘤区域.通过对多组病人的CT图像进行实验,结果显示该算法对肝脏肿瘤的分割效果良好.
基金Supported by the College of Medicine Research Centre,Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia
文摘BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liv
文摘目的肝纤维化是众多慢性肝脏疾病的常见表现,如不及时治疗可发展为肝硬化甚至引发肝癌。肝纤维化的准确评估对临床治疗和预后评估等至关重要。目前,肝纤维化的诊断通过肝穿活检判断,有创且有并发症危险。为此,基于影像学的无创诊断方法越来越受到关注。本文提出一种基于通道注意力与空间注意力机制改进的用于肝纤维化区域的自动化分割U-Net(liver fibrosis region segmentation network based on spatial and channel attention mechanisms, LFSCA-UNet)。方法依据Attention U-Net的改进方式,围绕U-Net的跳跃连接结构进行基于注意力的改进,在AG(attention gate)的基础上,加入以ECA(efficient channel attention)模块为实现方式的通道注意力机制,依据加入ECA的位置,LFSCA-UNet分为A、B、C共3个子型。结果在肝数据集上与其他实验网络进行评估对比,本文提出的LFSCA-UNet网络结构平均Dice系数达到了93.33%,相比原始U-Net的Dice系数提高了0.539 6%。结论本文方法将空间注意力机制与通道注意力机制进行结合,有效提高了肝纤维化区域的分割精度,对空间注意力模块使用通道注意力模块优化输入和输出,增加了网络的稳定性,提升了网络的整体效果。
基金the“Intelligent Recognition Industry Service Center”as part of the Featured Areas Research Center Program under the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan,and the National Science and Technology Council,Taiwan,under grants 113-2221-E-224-041 and 113-2622-E-224-002.Additionally,partial support was provided by Isuzu Optics Corporation.
文摘Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.
文摘显式模型下建立的肝脏分割算法受适用条件的限制,不能有效地控制分割进程,制约了算法鲁棒性和泛化能力的提高.针对这一问题,该文提出了一个新的方法,将肝脏分割问题转化为隐式函数最优值搜索,依据图像实际数据和先验形状信息有效地控制分割进程,以迭代搜索策略得到肝脏的最优分割结果.每一个迭代步骤由两部分组成:首先,利用MRF(Markov Random Field)实现已有肝脏轮廓邻域的局部再分割,重新调整肝脏轮廓;然后,利用先验形状稀疏表示调整后的形状,有效地修正噪声、边界模糊等因素引起的错误分割,并将修正后的肝脏轮廓用于下一轮局部再分割.肝脏分割结果在迭代过程中不断逼近真实值.为了提高形状稀疏表示的计算精度和速度,该文改进了相关技术细节,提出了动态字典生成策略,并利用混合粒子群算法求解稀疏表示方程.与金标准相比,该文所提算法在边界模糊、对比度低、存在大量其他干扰组织区域等不利因素的情景中,其分割精度(Volume Overlap Percentage,VOE)可达到90%以上.
