IEEE Trans Biomed Eng. 2018 Sep;65(9):1900-1911. doi: 10.1109/TBME.2018.2822826. Epub 2018 Apr 4.
Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature.
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.
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.
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.
分析现代大规模、多中心或疾病数据需要能够处理不同性质数据的可变形配准算法。
我们提出了一种新的基于线索感知深度回归网络的可变形配准方法，以最小化参数调整来处理多个数据库。
我们的方法学习和预测参考图像和主体图像之间的变形场。具体来说，给定一组训练图像，我们的方法学习与一对参考-主体补丁相关联的位移向量。为此，我们首先引入了关键点截断平衡采样策略，以方便从有限大小的图像数据库中进行准确学习。然后，我们设计了一个线索感知深度回归网络，其中我们提出使用上下文线索，即尺度自适应局部相似性，更明显地指导学习过程。深度回归网络对上下文线索有感知，能够准确预测局部变形。
我们的实验表明，所提出的方法可以处理不同数据库上的各种配准任务，无需手动参数调整即可提供一致的良好性能，这可能适用于各种临床应用。
