周周复始 (2023-01-31 20:41):
#paper 《Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration》,MICCAI,2018,https://doi.org/10.1007/978-3-030-00928-1_82 传统的可形变配准方法虽然有很好的效果和严格的理论证明,但由于是对每个图像对进行优化,计算量很大。而基于学习的方法虽然通过学习空间形变函数提高了配准速度,但限制了形变模型:需要监督标签,可能不保证微分同胚。因此本文提出一种使用微分图像配准的概率生成模型,推导出使用CNN和直观损失函数的学习算法,还引入了缩放和平方层。实现了快速有效的运算,保证了微分同胚并提供了不确定性估计。
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
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Abstract:
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu.
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