We introduce an end-to-end deep-learning framework for 3D medical image
registration. In contrast to existing approaches, our framework combines two
registration methods: an affine registration and a vector
momentum-parameterized stationary velocity field (vSVF) model. Specifically, it
consists of three stages. In the first stage, a multi-step affine network
predicts affine transform parameters. In the second stage, we use a Unet-like
network to generate a momentum, from which a velocity field can be computed via
smoothing. Finally, in the third stage, we employ a self-iterable map-based
vSVF component to provide a non-parametric refinement based on the current
estimate of the transformation map. Once the model is trained, a registration
is completed in one forward pass. To evaluate the performance, we conducted
longitudinal and cross-subject experiments on 3D magnetic resonance images
(MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show
that our framework achieves comparable performance to state-of-the-art medical
image registration approaches, but it is much faster, with a better control of
transformation regularity including the ability to produce approximately
symmetric transformations, and combining affine and non-parametric
registration.

本文介绍了一种基于深度学习框架的三维医学图像配准方法，结合仿射配准和向量动量参数固定速度场（vSVF）模型，具有速度快、精度高、可对称变换等特点，在磁共振成像方面表现出了优异的性能。

联合仿射和非参数图像配准的网络

Networks for Joint Affine and Non-parametric Image Registration

3D medical image registration is of great clinical importance. However,
supervised learning methods require a large amount of accurately annotated
corresponding control points (or morphing), which are very difficult to obtain.
Unsupervised learning methods ease the burden of manual annotation by
exploiting unlabeled data without supervision. In this paper, we propose a new
unsupervised learning method using convolutional neural networks under an
end-to-end framework, Volume Tweening Network (VTN), for 3D medical image
registration. We propose three innovative technical components: (1) An
end-to-end cascading scheme that resolves large displacement; (2) An efficient
integration of affine registration network; and (3) An additional invertibility
loss that encourages backward consistency. Experiments demonstrate that our
algorithm is 880x faster (or 3.3x faster without GPU acceleration) than
traditional optimization-based methods and achieves state-of-theart performance
in medical image registration.

该研究论文提出了一种新的无监督学习方法，使用卷积神经网络在端到端框架下进行三维医学图像配准，通过三个创新技术组件实现高性能，并且实验证明其在医学图像配准方面优于传统的基于优化的方法。

无监督三维端到端医学图像配准与体积缓动网络

Unsupervised 3D End-to-End Medical Image Registration with Volume  Tweening Network

We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
this https URL .

本文提出了一种基于学习的算法，用于变形、两两匹配的三维医学图像注册，通过使用卷积神经网络（CNN）模型并利用一个空间变换层，从而实现快速计算新的扫描成像时的配准场，且无需监督信息。我们可以在不破坏匹配场平滑性的前提下，底层地在一张图像中重构另一张图像，并在实践中将其速度提高数个数量级以上，从而有望显著加速医学图像分析和处理管道的速度，同时促进学习医学图像配准及其应用的新方向。