为了解决CT图像中由于胰腺体积小、位置和形状个体差异性较大导致的分割精度不高的问题,本文提出一种基于改进SegFormer模型的胰腺图像分割方法。在模型训练之前,根据胰腺的位置分布来构建候选区域并进行裁剪,从而有效减少背景区域的干...为了解决CT图像中由于胰腺体积小、位置和形状个体差异性较大导致的分割精度不高的问题,本文提出一种基于改进SegFormer模型的胰腺图像分割方法。在模型训练之前,根据胰腺的位置分布来构建候选区域并进行裁剪,从而有效减少背景区域的干扰,降低输入图像分辨率;接着采用SegFormer网络,并引入增大编码分辨率策略,通过调整下采样的比例来增大编码器输出特征图的尺寸,保留更多的细节信息,使模型能更好地应对胰腺的形态变化;然后引入残差极化自注意力模块对编码特征进行通道和空间注意力计算,以突出胰腺区域的关键特征,抑制无关特征的激活,从而提高模型的分割精度。本文方法在NIH数据集上测试的平均DSC为85.5%,参数量和计算量分别为3.91 M和6.89 G FLOPs,表明了该方法在胰腺分割任务上的有效性及其临床应用的潜力。展开更多
Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of inte...Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.展开更多
文摘为了解决CT图像中由于胰腺体积小、位置和形状个体差异性较大导致的分割精度不高的问题,本文提出一种基于改进SegFormer模型的胰腺图像分割方法。在模型训练之前,根据胰腺的位置分布来构建候选区域并进行裁剪,从而有效减少背景区域的干扰,降低输入图像分辨率;接着采用SegFormer网络,并引入增大编码分辨率策略,通过调整下采样的比例来增大编码器输出特征图的尺寸,保留更多的细节信息,使模型能更好地应对胰腺的形态变化;然后引入残差极化自注意力模块对编码特征进行通道和空间注意力计算,以突出胰腺区域的关键特征,抑制无关特征的激活,从而提高模型的分割精度。本文方法在NIH数据集上测试的平均DSC为85.5%,参数量和计算量分别为3.91 M和6.89 G FLOPs,表明了该方法在胰腺分割任务上的有效性及其临床应用的潜力。
基金funded by the National Natural Science Foundation of China(52004201,52274143,52204153)China Postdoctoral Science Foundation(2021T140551).
文摘Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.
