LDRNet: Large Deformation Registration Model for Chest CT Registration

TL;DR

LDRNet is a fast, unsupervised deep learning model designed specifically for large deformation chest CT image registration, outperforming traditional and existing deep learning methods in accuracy and speed.

Contribution

The paper introduces LDRNet, a novel unsupervised deep learning approach with innovative refine and rigid blocks for improved large deformation chest CT registration.

Findings

Achieves state-of-the-art registration accuracy.

Significantly faster than traditional methods.

Effective on both private and public datasets.

Abstract

Most of the deep learning based medical image registration algorithms focus on brain image registration tasks.Compared with brain registration, the chest CT registration has larger deformation, more complex background and region over-lap. In this paper, we propose a fast unsupervised deep learning method, LDRNet, for large deformation image registration of chest CT images. We first predict a coarse resolution registration field, then refine it from coarse to fine. We propose two innovative technical components: 1) a refine block that is used to refine the registration field in different resolutions, 2) a rigid block that is used to learn transformation matrix from high-level features. We train and evaluate our model on the private dataset and public dataset SegTHOR. We compare our performance with state-of-the-art traditional registration methods as well as deep learning registration models VoxelMorph, RCN, and LapIRN. The results demonstrate that our model achieves state-of-the-art performance for large deformation images registration and is much faster.