NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS
一、特點 自監督（相似性度量） multi-resolution image registration framework 二、Method 本文的框架如下圖所示： 可以看到網絡的輸入是Moving image and Fixed Image。loss 是多級loss。其中在不同的網絡部分計算一個 Dvloss。其中網絡的迴歸輸出（reg）用卷積來模擬迴歸輸出。迴歸層的輸出大小和輸入圖像大小一樣。
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相關文章
- 1. 【論文筆記】Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks
- 2. Image Registration
- 3. Fast Image Processing with Fully-Convolutional Networks
- 4. Deformable MRI-Ultrasound Registration Using 3D Convolutional Neural Network
- 5. Weakly-supervised convolutional neural networks for multimodal image registration
- 6. Registration小結2
- 7. Fully Convolutional Networks
- 8. 論文筆記（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