The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain ...The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain oceanic internal wave stripes from synthetic aperture radar(SAR)images,a stripe segmentation algorithm is proposed based on the TransUNet framework,which is a combination of U-Net and Transformer,which is also optimized.Through adjusting the number of Transformer layer,multi-layer perceptron(MLP)channel,and Dropout parameters,the influence of over-fitting on accuracy is significantly weakened,which is more conducive to segmenting lightweight oceanic internal waves.The results show that the optimized algorithm can accurately segment oceanic internal wave stripes.Moreover,the optimized algorithm can be trained on a microcomputer,thus reducing the research threshold.The proposed algorithm can also change the complexity of the model to adapt it to different date scales.Therefore,TransUNet has immense potential for segmenting oceanic internal waves.展开更多
医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取...医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取不同尺度的特征,融合两个分支的特征,实现特征互补;针对在融合两个分支的特征时可能存在冗余信息问题,添加多核并行池化模块,在保留多尺度特征的同时去除冗余信息;在解码器设计多尺度融合模块(USF),融合来自编码器的三个尺度信息,有效弥补编码器与解码器之间的信息差距。在Synapse和ACDC数据集上进行了多次对比实验,Synapse数据集上平均DSC系数可达79.20%,较TransUNet模型提高3.34%,HD距离为25.24%,降低了11.67%;在ACDC数据集上平均DSC系数可达90.30%,提高1.67%。In the medical image segmentation task, in order to make full use of the advantages of TransUNet model which can effectively capture global and local features, the DouTransNet model is proposed based on it. The encoder part aims at the problems of single learning Angle and easy loss of detail features in single-branch Transformer module. Transformer is designed as a two-branch parallel structure to extract features of different scales and fuse features of two branches to achieve feature complementarity. To solve the problem of redundant information when fusing the features of two branches, a multi-core parallel pooling module is added to remove redundant information while retaining multi-scale features. In the decoder design multi-scale fusion module (USF), the information of three scales from the encoder is fused to effectively bridge the information gap between the encoder and the decoder. Several comparison experiments were conducted on Synapse and ACDC data sets. The average DSC coefficient on Synapse data set can reach 79.20%, which is 3.34% higher than TransUNet model, and the HD distance is 25.24%, which is 11.67% lower. The average DSC coefficient on ACDC dataset can reach 90.30%, an increase of 1.67%.展开更多
目的介绍一种基于改进型TransUNet的高精度半月板自动分割模型。方法基于斯坦福大学公开的MRNet数据集,构建了一个由经验丰富的放射科医师精细标注的半月板分割子集作为训练与评估样本,将数据集按8∶2划分为训练集和测试集,通过迁移学...目的介绍一种基于改进型TransUNet的高精度半月板自动分割模型。方法基于斯坦福大学公开的MRNet数据集,构建了一个由经验丰富的放射科医师精细标注的半月板分割子集作为训练与评估样本,将数据集按8∶2划分为训练集和测试集,通过迁移学习建立了一个ECMA-TransUNet网络模型,设计了高效通道与多尺度注意力模块(efficient channel and multi-scale attention,ECMA),并将其嵌入TransUNet架构中的CNN编码器各阶段及解码器的3条跳跃连接路径中,在此模型上分割半月板的4个半月板角。结果本模型在4个解剖区域中均获得的分割性能如下:外侧半月板前角(DSC=87.43%,HD=0.5678)、外侧半月板后角(DSC=93.15%,HD=0.8455)、内侧半月板前角(DSC=91.48%,HD=0.7551)、内侧半月板后角(DSC=94.00%,HD=0.7407),各项指标均达到临床实用标准。结论本研究提出的ECMA-TransUNet模型在以下方面具有显著优势:(1)所引入的注意力模块提升了边界对齐精度与小结构识别能力;(2)模型的解剖精度满足临床诊断需求,有望为智能辅助诊断系统提供可靠性支持。展开更多
基金The National Natural Science Foundation of China under contract No.51679132the Science and Technology Commission of Shanghai Municipality under contract Nos.21ZR1427000 and 17040501600.
文摘The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data.Oceanic internal waves play a crucial role in oceanic activity.To obtain oceanic internal wave stripes from synthetic aperture radar(SAR)images,a stripe segmentation algorithm is proposed based on the TransUNet framework,which is a combination of U-Net and Transformer,which is also optimized.Through adjusting the number of Transformer layer,multi-layer perceptron(MLP)channel,and Dropout parameters,the influence of over-fitting on accuracy is significantly weakened,which is more conducive to segmenting lightweight oceanic internal waves.The results show that the optimized algorithm can accurately segment oceanic internal wave stripes.Moreover,the optimized algorithm can be trained on a microcomputer,thus reducing the research threshold.The proposed algorithm can also change the complexity of the model to adapt it to different date scales.Therefore,TransUNet has immense potential for segmenting oceanic internal waves.
文摘医学图像分割任务中,为充分利用TransUNet模型能有效捕获全局和局部特征的优势,在其基础上,提出DouTransNet模型,编码器部分针对单分支Transformer模块学习角度单一、容易丢失细节特征的问题,将Transformer设计成双分支并行结构,来提取不同尺度的特征,融合两个分支的特征,实现特征互补;针对在融合两个分支的特征时可能存在冗余信息问题,添加多核并行池化模块,在保留多尺度特征的同时去除冗余信息;在解码器设计多尺度融合模块(USF),融合来自编码器的三个尺度信息,有效弥补编码器与解码器之间的信息差距。在Synapse和ACDC数据集上进行了多次对比实验,Synapse数据集上平均DSC系数可达79.20%,较TransUNet模型提高3.34%,HD距离为25.24%,降低了11.67%;在ACDC数据集上平均DSC系数可达90.30%,提高1.67%。In the medical image segmentation task, in order to make full use of the advantages of TransUNet model which can effectively capture global and local features, the DouTransNet model is proposed based on it. The encoder part aims at the problems of single learning Angle and easy loss of detail features in single-branch Transformer module. Transformer is designed as a two-branch parallel structure to extract features of different scales and fuse features of two branches to achieve feature complementarity. To solve the problem of redundant information when fusing the features of two branches, a multi-core parallel pooling module is added to remove redundant information while retaining multi-scale features. In the decoder design multi-scale fusion module (USF), the information of three scales from the encoder is fused to effectively bridge the information gap between the encoder and the decoder. Several comparison experiments were conducted on Synapse and ACDC data sets. The average DSC coefficient on Synapse data set can reach 79.20%, which is 3.34% higher than TransUNet model, and the HD distance is 25.24%, which is 11.67% lower. The average DSC coefficient on ACDC dataset can reach 90.30%, an increase of 1.67%.
文摘目的介绍一种基于改进型TransUNet的高精度半月板自动分割模型。方法基于斯坦福大学公开的MRNet数据集,构建了一个由经验丰富的放射科医师精细标注的半月板分割子集作为训练与评估样本,将数据集按8∶2划分为训练集和测试集,通过迁移学习建立了一个ECMA-TransUNet网络模型,设计了高效通道与多尺度注意力模块(efficient channel and multi-scale attention,ECMA),并将其嵌入TransUNet架构中的CNN编码器各阶段及解码器的3条跳跃连接路径中,在此模型上分割半月板的4个半月板角。结果本模型在4个解剖区域中均获得的分割性能如下:外侧半月板前角(DSC=87.43%,HD=0.5678)、外侧半月板后角(DSC=93.15%,HD=0.8455)、内侧半月板前角(DSC=91.48%,HD=0.7551)、内侧半月板后角(DSC=94.00%,HD=0.7407),各项指标均达到临床实用标准。结论本研究提出的ECMA-TransUNet模型在以下方面具有显著优势:(1)所引入的注意力模块提升了边界对齐精度与小结构识别能力;(2)模型的解剖精度满足临床诊断需求,有望为智能辅助诊断系统提供可靠性支持。
