NCF: Neural Correspondence Field for Medical Image Registration
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
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.
