Deformable templates are essential to large-scale medical image registration,
segmentation, and population analysis. Current conventional and deep
network-based methods for template construction use only regularized
registration objectives and often yield templates with blurry and/or
anatomically implausible appearance, confounding downstream biomedical
interpretation. We reformulate deformable registration and conditional template
estimation as an adversarial game wherein we encourage realism in the moved
templates with a generative adversarial registration framework conditioned on
flexible image covariates. The resulting templates exhibit significant gain in
specificity to attributes such as age and disease, better fit underlying
group-wise spatiotemporal trends, and achieve improved sharpness and
centrality. These improvements enable more accurate population modeling with
diverse covariates for standardized downstream analyses and easier anatomical
delineation for structures of interest.

通过在生成敌对式登录框架中利用灵活的图像协变量 - 使得移动的模板更真实，作者重新制定了可变形模板，以获得更好的特异性和适应性，该模板能更好地拟合潜在的群体空间 - 时间趋势，并使解剖学结构更容易划分。

生成对抗式注册方法用于改进条件变形模板

Generative Adversarial Registration for Improved Conditional Deformable  Templates

We develop a learning framework for building deformable templates, which play
a fundamental role in many image analysis and computational anatomy tasks.
Conventional methods for template creation and image alignment to the template
have undergone decades of rich technical development. In these frameworks,
templates are constructed using an iterative process of template estimation and
alignment, which is often computationally very expensive. Due in part to this
shortcoming, most methods compute a single template for the entire population
of images, or a few templates for specific sub-groups of the data. In this
work, we present a probabilistic model and efficient learning strategy that
yields either universal or conditional templates, jointly with a neural network
that provides efficient alignment of the images to these templates. We
demonstrate the usefulness of this method on a variety of domains, with a
special focus on neuroimaging. This is particularly useful for clinical
applications where a pre-existing template does not exist, or creating a new
one with traditional methods can be prohibitively expensive. Our code and
atlases are available online as part of the VoxelMorph library at
this http URL

本文介绍了一种利用概率模型和神经网络构建变形模板的学习框架，具有广泛的图像分析和计算解剖学任务应用，特别适用于临床应用中不存在预先存在模板的情况。