Accurate volumetric image registration is highly relevant for clinical
routines and computer-aided medical diagnosis. Recently, researchers have begun
to use transformers in learning-based methods for medical image registration,
and have achieved remarkable success. Due to the strong global modeling
capability, Transformers are considered a better option than convolutional
neural networks (CNNs) for registration. However, they use bulky models with
huge parameter sets, which require high computation edge devices for deployment
as portable devices or in hospitals. Transformers also need a large amount of
training data to produce significant results, and it is often challenging to
collect suitable annotated data. Although existing CNN-based image registration
can offer rich local information, their global modeling capability is poor for
handling long-distance information interaction and limits registration
performance. In this work, we propose a CNN-based registration method with an
enhanced receptive field, a low number of parameters, and significant results
on a limited training dataset. For this, we propose a residual U-Net with
embedded parallel dilated-convolutional blocks to enhance the receptive field.
The proposed method is evaluated on inter-patient and atlas-based datasets. We
show that the performance of the proposed method is comparable and slightly
better than transformer-based methods by using only $\SI{1.5}{\percent}$ of its
number of parameters.

使用改进的卷积神经网络进行体积图像配准，提供增强的感受野，减少参数并在受限的训练数据集上取得与基于 Transformer 方法相当甚至稍好的性能。