A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN ...A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.展开更多
针对现有分割方法难以兼顾分割精度和复杂度的问题,提出了一种新型轻量化结直肠息肉图像分割网络MCANet(Mamba and convolutional attention network).该网络的核心在于级联了空间注意力和通道注意力的多尺度卷积注意力模块,通过融合多...针对现有分割方法难以兼顾分割精度和复杂度的问题,提出了一种新型轻量化结直肠息肉图像分割网络MCANet(Mamba and convolutional attention network).该网络的核心在于级联了空间注意力和通道注意力的多尺度卷积注意力模块,通过融合多尺度特征,以缩减浅层和深层特征之间的差距.此外,引入了并行Mamba模块,利用并行化计算的方式提高运算效率.在3个公共数据集上的实验结果表明,所提出的方法在有效性和泛化方面都优于其他先进的方法,使其能够精准地定位结直肠中的异常部分,为临床医师提供关键的决策支持,从而降低了息肉癌变的风险.展开更多
在结直肠癌的早期筛查中,通过对结肠镜图像进行自动化的息肉检测和分割可以提高诊断效率和准确性。由于肠道内部环境的复杂性以及图像质量的限制,自动化的息肉分割仍然是一个具有挑战性的问题。针对这一问题,提出了一种基于Transformer...在结直肠癌的早期筛查中,通过对结肠镜图像进行自动化的息肉检测和分割可以提高诊断效率和准确性。由于肠道内部环境的复杂性以及图像质量的限制,自动化的息肉分割仍然是一个具有挑战性的问题。针对这一问题,提出了一种基于Transformer和空洞卷积特征融合的息肉分割双解码模型(Dual decoded polyp segmentation model fusing Transformer and dilated convolution,FTDC-Net)。该模型以ResNet50作为编码器,以便能够更好地提取图像深层次特征。使用Transformer编码模块,它的自注意力(Self-attention)机制能够捕捉输入之间的长距离依赖关系,模型中使用了不同的空洞卷积(Dilated-convolution)来扩大模型的感受野,让模型能捕捉到结肠镜图像更大范围内的信息。本文网络模型的解码部分使用双解码结构,包含一个自动编码器分支,自动编码器可以重构输入,另一个编码分支用于分割结果。模型中,自动编码器的输出被用于生成一个注意力图作为注意力机制,该图将被用于指导分割结果。在Kvasir-SEG和ETIS-LARIBPOLYPDB标准数据集上进行了实验验证,实验结果表明FTDC-Net能有效地分割出结肠息肉,相比目前主流息肉分割模型,在各项评价指标上均取得了较高的提升。展开更多
基金Projects(61172002,61001047,60671050)supported by the National Natural Science Foundation of ChinaProject(N100404010)supported by Fundamental Research Grant Scheme for the Central Universities,China
文摘A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.
文摘针对现有分割方法难以兼顾分割精度和复杂度的问题,提出了一种新型轻量化结直肠息肉图像分割网络MCANet(Mamba and convolutional attention network).该网络的核心在于级联了空间注意力和通道注意力的多尺度卷积注意力模块,通过融合多尺度特征,以缩减浅层和深层特征之间的差距.此外,引入了并行Mamba模块,利用并行化计算的方式提高运算效率.在3个公共数据集上的实验结果表明,所提出的方法在有效性和泛化方面都优于其他先进的方法,使其能够精准地定位结直肠中的异常部分,为临床医师提供关键的决策支持,从而降低了息肉癌变的风险.
文摘在结直肠癌的早期筛查中,通过对结肠镜图像进行自动化的息肉检测和分割可以提高诊断效率和准确性。由于肠道内部环境的复杂性以及图像质量的限制,自动化的息肉分割仍然是一个具有挑战性的问题。针对这一问题,提出了一种基于Transformer和空洞卷积特征融合的息肉分割双解码模型(Dual decoded polyp segmentation model fusing Transformer and dilated convolution,FTDC-Net)。该模型以ResNet50作为编码器,以便能够更好地提取图像深层次特征。使用Transformer编码模块,它的自注意力(Self-attention)机制能够捕捉输入之间的长距离依赖关系,模型中使用了不同的空洞卷积(Dilated-convolution)来扩大模型的感受野,让模型能捕捉到结肠镜图像更大范围内的信息。本文网络模型的解码部分使用双解码结构,包含一个自动编码器分支,自动编码器可以重构输入,另一个编码分支用于分割结果。模型中,自动编码器的输出被用于生成一个注意力图作为注意力机制,该图将被用于指导分割结果。在Kvasir-SEG和ETIS-LARIBPOLYPDB标准数据集上进行了实验验证,实验结果表明FTDC-Net能有效地分割出结肠息肉,相比目前主流息肉分割模型,在各项评价指标上均取得了较高的提升。
