Abstract
Learning-based techniques have significantly improved the accuracy and speed of deformable image registration. However, challenges such as reducing computational complexity and handling large deformations persist. To address these challenges, we analyze how convolutional neural networks (ConvNets) influence registration performance using the Horn-Schunck optical flow equation. Supported by prior studies and our empirical experiments, we observe that ConvNets play two key roles in registration: linearizing local intensities and harmonizing global contrast variations. Based on these insights, we propose the Encoder-Only Image Registration (EOIR) framework, designed to achieve a better accuracy-efficiency trade-off. EOIR separates feature learning from flow estimation, employing only a 3-layer ConvNet for feature extraction and a set of 3-layer flow estimators to construct a Laplacian feature pyramid, progressively composing diffeomorphic deformations under a large-deformation model. Results on five datasets across different modalities and anatomical regions demonstrate EOIR's effectiveness, achieving superior accuracy-efficiency and accuracy-smoothness trade-offs. With comparable accuracy, EOIR provides better efficiency and smoothness, and vice versa. The source code of EOIR will be publicly available on this https URL.
Abstract (translated)
基于学习的技术显著提高了可变形图像配准的准确性和速度。然而，减少计算复杂度和处理大形变等问题仍然存在挑战。为了解决这些问题，我们分析了卷积神经网络（ConvNets）如何通过Horn-Schunck光流方程影响配准性能。基于先前的研究和我们的实验证据，我们观察到ConvNets在图像配准中扮演两个关键角色：线性化局部强度以及协调全局对比度变化。根据这些见解，我们提出了编码器仅有的图像配准（EOIR）框架，旨在实现更好的准确性和效率之间的权衡。EOIR将特征学习与流估计分离，使用一个三层的卷积网络进行特征提取，并采用一组三层流估算器来构建拉普拉斯特征金字塔，在大形变模型下逐步组合微分同胚变形。 在五种不同模式和解剖区域的数据集上的结果表明了EOIR的有效性，实现了更优的准确性和效率权衡以及准确性和平滑度之间的最佳平衡。与相当的准确性相比，EOIR提供更好的效率和平滑度；反之亦然。 EOIR的源代码将在[此链接](https://example.com)公开发布（请将URL替换为实际网站地址）。
URL
https://arxiv.org/abs/2509.00451
