NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS
一、特点 自监督（相似性度量） multi-resolution image registration framework 二、Method 本文的框架如下图所示： 可以看到网络的输入是Moving image and Fixed Image。loss 是多级loss。其中在不同的网络部分计算一个 Dvloss。其中网络的回归输出（reg）用卷积来模拟回归输出。回归层的输出大小和输入图像大小一样。
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相关文章
- 1. 【论文笔记】Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks
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- 5. Weakly-supervised convolutional neural networks for multimodal image registration
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- 7. Fully Convolutional Networks
- 8. 论文笔记（十二）Tversky loss function for image segmentation using 3D fully convolutional deep networks
- 9. Transposed convolution & Fully Convolutional Neural Network
- 10. Fully Convolutional Adaptation Networks for Semantic Segmentation