在此数据集上进行训练,网络可以密集地分割新的体积图像。 通过将所有2D操作替换为3D操作,网络扩展了Ronneberger等人先前的U-Net体系结构。该实现执行实时弹性变形,以在训练期间进行有效的数据增强。 网络在复杂的,高度可变的3D结构(非洲爪蟾肾脏)上测试了性能,并在两种使用情况下均取得了较好的结果。 网络结构 在许多...
HighResNet 它使用一系列带有残差连接的3D卷积层。该模型是端到端训练的，可以一次处理整个3D图像。EfficientNet3D 这是对EfficientNet架构的3D改进，它不像U-Net或V-Net那样常用于3D分割，但如果计算资源有限，它是可以考虑的，因为它在计算成本和性能之间的良好权衡。Attention U-Net 这是U-Net的一种变体，它包含...
The dice of 3D U-net, 3D Attention U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881,0.884,0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in ...
Attention U-Net 这是U-Net的一种变体,它包含了一个注意力机制,允许网络将注意力集中在与手头任务更相关的图像的某些部分。 DeepMedic 这是一个使用双路径的3D CNN,一个是正常分辨率,另一个是下采样输入,这样可以结合局部和更大的上下文信息。 总结 本文中,我们介绍了医学成像行业在处理3D MRI和CT扫描时使用的...
The geological model reconstructed by the 3D U-Net model with attention mechanism still has a certain deviation from the original model. Considering that the model is further adjusted in the process of history matching based on the production performance, the more critical indicator for evaluating th...
实验结果表明,基于多尺度融合3D U-Net感染区域分割网络在肺部图像分割中,对肺背景,左右肺,感染区域的平均Dice系数为99.76%,95.54%,77.24%,灵敏度分别为99.79%,96.28%,73.83%,特异性分别为97.82%,99.78%,99.85%.该算法在COVID-19胸部CT图像的自动分割中具有良好的性能.本研究推动了CT图像中COVID-19肺部感染的...
The dice of 3D U-net, 3D Attention U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in ...
An attention U-Net improved performance of segmentation of pancreas of various shapes and small sizes, by using an AM with 1 × 1 convolution layer and a sigmoid activation function to reduce background weight and to preserve foreground weight. An Attention U-Net++ improved liver ...
The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects. The results show that compared with CNN-PCA method, the 3D attention U-Net network could better complement the details of geological model lost in the PCA...
The proposed ACU-Net was evaluated on the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, and it achieved superior performance compared to latest approaches. 展开 关键词: Multiple sclerosis lesion segmentation U-Net spatial attention context guided ...