Semantically Guided Large Deformation Estimation with Deep Networks
Semantically Guided Large Deformation Estimation with Deep Networks
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DOI：
10.3390/s20051392
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发表时间：
2020-03-01
期刊：
影响因子：
3.9
通讯作者：
Heinrich, Mattias
中科院分区：
文献类型：
--
作者：
Ha, In Young;Wilms, Matthias;Heinrich, Mattias
当所考虑的图像具有强烈的外观变化和较大的初始未对准时，可变形图像配准仍然是一个挑战。目前，对于视频中的快速移动区域或自然物体的强烈变形，仍然存在巨大的性能差距。我们提出了一种新的语义引导的两步深度变形网络，特别适合大变形的估计。我们将弱监督的 U-Net 架构与分割信息相结合，通过低维 B 样条变形参数化的多级非刚性空间变换器网络来提取语义上有意义的特征。将对齐损失和语义损失函数与正则化惩罚相结合以获得平滑且合理的变形，与之前仅考虑标签驱动的对齐损失的方法相比，我们在对齐质量方面取得了优异的结果。与 FlowNet 和 Label-Reg 这两个最新的深度学习配准框架相比，我们的网络模型推进了医学心脏磁共振成像 (MRI) 序列中受试者间面部部分对齐和运动跟踪的最先进技术。这些模型结构紧凑，推理速度非常快，并且在计算机视觉和医学图像分析中的各种具有挑战性的跟踪和/或对齐任务中表现出明显的潜力。
Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer networks parameterized with low-dimensional B-spline deformations. Combining alignment loss and semantic loss functions together with a regularization penalty to obtain smooth and plausible deformations, we achieve superior results in terms of alignment quality compared to previous approaches that have only considered a label-driven alignment loss. Our network model advances the state of the art for inter-subject face part alignment and motion tracking in medical cardiac magnetic resonance imaging (MRI) sequences in comparison to the FlowNet and Label-Reg, two recent deep-learning registration frameworks. The models are compact, very fast in inference, and demonstrate clear potential for a variety of challenging tracking and/or alignment tasks in computer vision and medical image analysis.