Research on a 3D Lung Computed Tomography Image Registration Method Based on Unsupervised Learning

Deformable registration of 3D lung CT images is crucial in medical image registration. However, nonlinear deformation and large-scale displacement of lung tissues caused by respiratory motion pose great challenges in the deformable registration of 3D lung CT images. Thus, we present a fast end-to-end registration method based on unsupervised learning. We opti-mized the classic U-Net model and added Inception modules between skip connections. The Inception module aims to capture and merge information at different spatial scales for generating a high-precision dense displacement vector field. To ensure a smooth displacement vector field, we introduced the Jacobian regularization term into the loss function to directly penalize the singularity of the displacement field during training. The existing publicly available datasets cannot implement model training. To address over-fitting caused by limited data resources and to expand the training data, we proposed a data augmentation method based on a 3D thin plate spline transform. Moreover, 6060 CT scans will be generated based on the EMPIRE10 dataset, which contains 60 original CT scans to meet the requirement of convolution neural network training. Regarding the DIR-Lab 4DCT dataset, we achieved a target registration error of 2.09mm, an optimal Dice score of 0.987, and almost no folding voxels in comparison with the experimental results obtained using the deep learning method Voxelmorph and registration packages, such as advanced normalization tools (ANTs) and Elastix. © 2022, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.