Atlas building and image registration are important tasks for medical image
analysis. Once one or multiple atlases from an image population have been
constructed, commonly (1) images are warped into an atlas space to study
intra-subject or inter-subject variations or (2) a possibly probabilistic atlas
is warped into image space to assign anatomical labels. Atlas estimation and
nonparametric transformations are computationally expensive as they usually
require numerical optimization. Additionally, previous approaches for atlas
building often define similarity measures between a fuzzy atlas and each
individual image, which may cause alignment difficulties because a fuzzy atlas
does not exhibit clear anatomical structures in contrast to the individual
images. This work explores using a convolutional neural network (CNN) to
jointly predict the atlas and a stationary velocity field (SVF)
parameterization for diffeomorphic image registration with respect to the
atlas. Our approach does not require affine pre-registrations and utilizes
pairwise image alignment losses to increase registration accuracy. We evaluate
our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset.
Our results show that the proposed framework achieves better performance than
other state-of-the-art image registration algorithms, allows for end-to-end
training, and for fast inference at test time.

通过卷积神经网络同时预测畸变图和恒定速度场参数化，利用成对图像对齐损失以提高医学图像配准精度，实现了 3D 膝关节磁共振成像的快速、端到端训练和测试，且表现优于其他同类算法。