A lightweight residual network for unsupervised deformable image registration

TL;DR

This paper introduces a lightweight residual U-Net with dilated convolutions for unsupervised deformable image registration, achieving comparable or better results than transformer-based methods with significantly fewer parameters.

Contribution

A novel CNN-based registration network with enhanced receptive field and reduced parameters, outperforming transformer models on limited training data.

Findings

Achieves comparable or better registration accuracy than transformer methods.

Uses only 1.5% of the parameters of transformer models.

Performs well on inter-patient and atlas-based datasets.

Abstract

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.