NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA.
NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA.
复制标题
使用自制的完全卷积网络的非刚性图像注册，而无需训练数据。
DOI：
10.1109/isbi.2018.8363757
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Fan Y
中科院分区：
文献类型：
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作者：
Li H;Fan Y
提出了一种新的基于全卷积网络的非刚性图像配准算法，用于在自监督学习框架中优化和学习待配准图像对之间的空间变换。与大多数现有的基于深度学习的图像配准方法不同，该方法从已知对应的空间变换的训练数据中学习空间变换，与传统的图像配准算法类似，该方法通过最大化固定和变形的运动图像之间的图像相似性来直接估计图像对之间的空间变换。图像配准是在多分辨率图像配准框架中实现的，通过典型的前馈和反向传播计算，在深度自我监督的情况下联合优化和学习不同空间分辨率的空间变换和FCN。该方法在3D结构脑磁共振(MR)图像配准中取得了比现有图像配准算法更好的效果。
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.