Deformable Image Registration based on Similarity-Steered CNN Regression.
Deformable Image Registration based on Similarity-Steered CNN Regression.
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DOI：
10.1007/978-3-319-66182-7_35
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Cao X;Yang J;Zhang J;Nie D;Kim MJ;Wang Q;Shen D
现有的可变形配准方法需要穷尽迭代优化以及仔细的参数调整来估计图像之间的变形场。虽然已经提出了一些基于学习的方法来启动变形估计，但它们往往是特定于模板的，在实际应用中不灵活。本文提出了一种基于卷积神经网络(CNN)的回归模型来直接学习从输入图像对(即一对模板和对象)到它们对应的变形场的复映射。具体地说，我们的CNN架构是以基于面片的方式设计的，以学习从输入面片对到它们各自的变形场的复杂映射。首先，为了便于在有限的图像数据集上进行准确的CNN模型学习，引入了均衡活动点引导采样策略。然后，设计了相似度导向的CNN结构，在该结构中加入了辅助上下文线索，即输入块之间的相似度，以更直接地指导学习过程。在不同的脑图像数据集上的实验表明，基于我们的CNN模型的配准性能良好。此外，研究还发现，尽管不同数据集中的大脑外观非常不同，但从一个数据集中训练的CNN模型可以成功地转移到另一个数据集中。
Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.