在肠道息肉分割任务中,由于息肉病变区域与周围正常组织颜色对比度不高,且边界模糊,这导致分割过程中容易丢失关键信息,并受到噪声干扰,影响分割质量。为了解决这些问题,提出一种改进的U-Net模型EEPSNet。首先,EEPSNet通过将普通卷积与...在肠道息肉分割任务中,由于息肉病变区域与周围正常组织颜色对比度不高,且边界模糊,这导致分割过程中容易丢失关键信息,并受到噪声干扰,影响分割质量。为了解决这些问题,提出一种改进的U-Net模型EEPSNet。首先,EEPSNet通过将普通卷积与空洞卷积相结合,替换了原有的双卷积块。这种组合能捕捉更广泛区域的空间特征,从而减少信息丢失。其次,在解码阶段的特征融合过程中,在U-Net的基础上引入了全局注意力机制特征融合,这种融合方式不仅能够在空间和通道维度上关注显著特征,还能增强模型对噪声干扰的抑制能力。EEPSNet在单卡NVIDIA Quadro RTX 5000 GPU上对四个公开的息肉分割数据集进行了实验,包括Kvasir-SEG和CVC-ClinicDB,用于评估模型的特征建模能力,以及CVC-ColonDB和ETIS-LaribPolypDB,用于评估模型的泛化能力。实验结果表明,EEPSNet模型在这些数据集上均取得了显著的性能提升,mdice分别提高至88.6%、91.1%、70.8%和66.0%,同时也证明了EEPSNet在保持建模能力的同时,也具有良好的泛化能力。In the intestinal polyp segmentation task, due to the low color contrast of the polyp lesion area and the surrounding normal tissue and the blurred boundary, this leads to the loss of key information in the segmentation process and the interference of noise, which affects the segmentation quality. To solve these problems, a modified U-Net model EEPSNet is proposed. First, EEPSNet replaces the original double convolution block by combining ordinary convolution with void convolution. This combination captures the spatial features of a wider region, thereby reducing information loss. Secondly, in the feature fusion process in the decoding stage, global attention mechanism feature fusion is introduced on the basis of U-Net, and this fusion mode can not only focus on salient features in spatial and channel dimensions, but also enhance the ability of the model to suppress noise interference. EEPSNet experiments on four publicly available polyp segmentation datasets, including Kvasir-SEG and CVC-ClinicDB, to evaluate the feature modeling ability of the model, and CVC-ColonDB and ETIS-LaribPolypDB to evaluate the generalization ability of the model. The experimental results show that the EEPSNet model achieved significant performance improvement in these datasets, and the mdice increased to 88.6%, 91.1%, 70.8%, and 66.0%, respectively, which also proved that EEPSNet has good generalization ability while maintaining the modeling ability.展开更多
目的探讨SMRACAD1对胃癌细胞增殖和侵袭的影响及作用机制。方法对癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中373个胃癌组织和32个正常组织的SMRACAD1表达进行差异分析及京都基因与基因组百科全书(Kyoto Encyclopedia of Gen...目的探讨SMRACAD1对胃癌细胞增殖和侵袭的影响及作用机制。方法对癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中373个胃癌组织和32个正常组织的SMRACAD1表达进行差异分析及京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析,采用蛋白印迹法(Western blot,WB)和实时荧光定量聚合酶链反应(quantitative real-time polymerase chain reaction,qPCR)检测SMRACAD1在人正常胃黏膜上皮细胞(GES-1)、人胃癌细胞(HGC-27)和人胃腺癌细胞(AGS)的表达。构建AGS细胞下调SMRACAD1表达和空白对照模型,平板克隆和CCK-8实验检测AGS细胞的增殖能力,划痕实验检测迁移能力,Transwell实验检测迁移和侵袭能力。WB检测SMRACAD1下调后对上皮-间充质转化(epithelial-mesenchymal transition,EMT)相关蛋白表达(E-cadherin、N-cadherin、Vimentin、Snail)及PI3K/AKT/mTOR信号通路的影响。结果SMRACAD1在胃癌组织和AGS细胞中高表达。下调SMRACAD1表达抑制了AGS细胞增殖、侵袭和迁移能力(P<0.05),抑制了AGS细胞EMT过程和PI3K/AKT/mTOR信号通路的激活。结论SMARCDA1在胃癌组织和胃癌细胞系中表达上调,通过抑制PI3K/AKT/mTOR信号通路激活下调SMRACAD1表达可抑制胃癌细胞的增殖、侵袭、迁移和EMT。展开更多
文摘在肠道息肉分割任务中,由于息肉病变区域与周围正常组织颜色对比度不高,且边界模糊,这导致分割过程中容易丢失关键信息,并受到噪声干扰,影响分割质量。为了解决这些问题,提出一种改进的U-Net模型EEPSNet。首先,EEPSNet通过将普通卷积与空洞卷积相结合,替换了原有的双卷积块。这种组合能捕捉更广泛区域的空间特征,从而减少信息丢失。其次,在解码阶段的特征融合过程中,在U-Net的基础上引入了全局注意力机制特征融合,这种融合方式不仅能够在空间和通道维度上关注显著特征,还能增强模型对噪声干扰的抑制能力。EEPSNet在单卡NVIDIA Quadro RTX 5000 GPU上对四个公开的息肉分割数据集进行了实验,包括Kvasir-SEG和CVC-ClinicDB,用于评估模型的特征建模能力,以及CVC-ColonDB和ETIS-LaribPolypDB,用于评估模型的泛化能力。实验结果表明,EEPSNet模型在这些数据集上均取得了显著的性能提升,mdice分别提高至88.6%、91.1%、70.8%和66.0%,同时也证明了EEPSNet在保持建模能力的同时,也具有良好的泛化能力。In the intestinal polyp segmentation task, due to the low color contrast of the polyp lesion area and the surrounding normal tissue and the blurred boundary, this leads to the loss of key information in the segmentation process and the interference of noise, which affects the segmentation quality. To solve these problems, a modified U-Net model EEPSNet is proposed. First, EEPSNet replaces the original double convolution block by combining ordinary convolution with void convolution. This combination captures the spatial features of a wider region, thereby reducing information loss. Secondly, in the feature fusion process in the decoding stage, global attention mechanism feature fusion is introduced on the basis of U-Net, and this fusion mode can not only focus on salient features in spatial and channel dimensions, but also enhance the ability of the model to suppress noise interference. EEPSNet experiments on four publicly available polyp segmentation datasets, including Kvasir-SEG and CVC-ClinicDB, to evaluate the feature modeling ability of the model, and CVC-ColonDB and ETIS-LaribPolypDB to evaluate the generalization ability of the model. The experimental results show that the EEPSNet model achieved significant performance improvement in these datasets, and the mdice increased to 88.6%, 91.1%, 70.8%, and 66.0%, respectively, which also proved that EEPSNet has good generalization ability while maintaining the modeling ability.
文摘目的探讨SMRACAD1对胃癌细胞增殖和侵袭的影响及作用机制。方法对癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中373个胃癌组织和32个正常组织的SMRACAD1表达进行差异分析及京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析,采用蛋白印迹法(Western blot,WB)和实时荧光定量聚合酶链反应(quantitative real-time polymerase chain reaction,qPCR)检测SMRACAD1在人正常胃黏膜上皮细胞(GES-1)、人胃癌细胞(HGC-27)和人胃腺癌细胞(AGS)的表达。构建AGS细胞下调SMRACAD1表达和空白对照模型,平板克隆和CCK-8实验检测AGS细胞的增殖能力,划痕实验检测迁移能力,Transwell实验检测迁移和侵袭能力。WB检测SMRACAD1下调后对上皮-间充质转化(epithelial-mesenchymal transition,EMT)相关蛋白表达(E-cadherin、N-cadherin、Vimentin、Snail)及PI3K/AKT/mTOR信号通路的影响。结果SMRACAD1在胃癌组织和AGS细胞中高表达。下调SMRACAD1表达抑制了AGS细胞增殖、侵袭和迁移能力(P<0.05),抑制了AGS细胞EMT过程和PI3K/AKT/mTOR信号通路的激活。结论SMARCDA1在胃癌组织和胃癌细胞系中表达上调,通过抑制PI3K/AKT/mTOR信号通路激活下调SMRACAD1表达可抑制胃癌细胞的增殖、侵袭、迁移和EMT。
