仅用一对医学影像即可实现精准配准，且模型极小。
NCF: Neural Correspondence Field for Medical Image Registration
- 用一个图像对训练神经对应场，自适应生成专属参数。
- 仅0.06万参数，在肺部CT和头颈部数据上表现优于传统方法。
- 适合数据稀缺的临床场景，尤其适用于个性化配准任务。
可变形图像配准是医学图像处理的基础任务。传统基于优化的方法在复杂形变下精度不足，而学习型方法虽在公开数据集上表现良好，但受限于医学图像数据稀缺，难以构建泛化能力强的模型。为此，我们提出一种无需训练数据的学习方法——神经对应场（NCF），仅需一对图像即可训练。该方法采用紧凑的神经网络建模对应场，并为每对图像单独优化网络参数，使每对图像拥有唯一的权重集合。模型极为高效，仅含0.06百万参数。评估结果显示，该方法在公开肺部CT数据集上表现优异，且在头颈部数据集上超越传统方法，证明了其有效性与高效性。
原文摘要 · Abstract (English)
Deformable image registration is a fundamental task in medical image processing. Traditional optimization-based methods often struggle with accuracy in dealing with complex deformation. Recently, learning-based methods have achieved good performance on public datasets, but the scarcity of medical image data makes it challenging to build a generalizable model to handle diverse real-world scenarios. To address this, we propose a training-data-free learning-based method, Neural Correspondence Field (NCF), which can learn from just one data pair. Our approach employs a compact neural network to model the correspondence field and optimize model parameters for each individual image pair. Consequently, each pair has a unique set of network weights. Notably, our model is highly efficient, utilizing only 0.06 million parameters. Evaluation results showed that the proposed method achieved superior performance on a public Lung CT dataset and outperformed a traditional method on a head and neck dataset, demonstrating both its effectiveness and efficiency.
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