用轻量编码器实现高精度快速图像配准，兼顾效率与平滑性。
Encoder-Only Image Registration
- 仅用3层卷积网络提取特征，分离特征学习与光流估计
- 在五个数据集上达到更优的精度-效率与精度-平滑度平衡
- 适合需要高效且稳定配准的医学图像分析场景
基于学习的方法显著提升了可变形图像配准的精度与速度，但计算复杂度高和处理大形变仍具挑战。本文通过分析卷积神经网络（ConvNets）对霍恩-施恩克光流方程的影响，发现其在配准中起两个关键作用：线性化局部强度与调和全局对比度变化。基于此，提出仅使用编码器的图像配准框架（EOIR），将特征学习与光流估计解耦。EOIR采用3层卷积网络进行特征提取，并构建拉普拉斯特征金字塔，通过多层3层光流估计算子逐步生成微分同胚形变。在跨模态、不同解剖区域的五个数据集上验证，EOIR在保持相当精度的前提下，实现更高的效率与更好的平滑性，反之亦然。源代码已公开于 https://github.com/XiangChen1994/EOIR。
原文摘要 · Abstract (English)
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 is publicly available on https://github.com/XiangChen1994/EOIR.
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