Deformable Image Registration Using a Cue-Aware Deep Regression Network.
Deformable Image Registration Using a Cue-Aware Deep Regression Network.
复制标题
使用提示感知深度回归网络的可变形图像配准
DOI：
10.1109/tbme.2018.2822826
复制
发表时间：
2018-09
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;Wang Q;Yap PT;Shen D
重要性：现代大规模、多中心或疾病数据的分析需要可变形配准算法，该算法可以科普不同性质的数据。目的：我们提出了一种新的变形配准方法，这是基于线索感知的深度回归网络，以最小的参数调整来处理多个数据库。方法：我们的方法学习和预测参考图像和主题图像之间的变形场。具体来说，给定一组训练图像，我们的方法学习与一对参考对象补丁相关联的位移向量。为了实现这一目标，我们首先引入了关键点截断平衡采样策略，以便于从有限大小的图像数据库中准确学习。然后，我们设计了一个线索感知的深度回归网络，我们建议使用上下文线索，即，尺度自适应的局部相似性，更明显地指导学习过程。深度回归网络知道用于准确预测局部变形的上下文线索。结果与结论：实验结果表明，该方法可以在不同的数据库上完成不同的配准任务，且不需要人工调整参数，具有良好的一致性，适用于各种临床应用。
Significance: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature. Objective: We propose a novel deformable registration method, which is based on a cue-aware deep regression network, to deal with multiple databases with minimal parameter tuning. Methods: Our method learns and predicts the deformation field between a reference image and a subject image. Specifically, given a set of training images, our method learns the displacement vector associated with a pair of reference–subject patches. To achieve this, we first introduce a key-point truncated-balanced sampling strategy to facilitate accurate learning from the image database of limited size. Then, we design a cue-aware deep regression network, where we propose to employ the contextual cue, i.e., the scale-adaptive local similarity, to more apparently guide the learning process. The deep regression network is aware of the contextual cue for accurate prediction of local deformation. Results and Conclusion: Our experiments show that the proposed method can tackle various registration tasks on different databases, giving consistent good performance without the need of manual parameter tuning, which could be applicable to various clinical applications.