4D‐CT deformable image registration using unsupervised recursive cascaded full‐resolution residual networks

Abstract

Abstract A novel recursive cascaded full‐resolution residual network (RCFRR‐Net) for abdominal four‐dimensional computed tomography (4D‐CT) image registration was proposed. The entire network was end‐to‐end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full‐resolution residual subnetworks with different architectures. The training loss consisted of the image similarity loss and the deformation vector field regularization loss, which were calculated based on the final warped image and the fixed image, allowing all cascades to be trained jointly and perform the progressive registration cooperatively. Extensive network testing was conducted using diverse datasets, including an internal 4D‐CT dataset, a public DIRLAB 4D‐CT dataset, and a 4D cone‐beam CT (4D‐CBCT) dataset. Compared with the iteration‐based demon method and two deep learning‐based methods (VoxelMorph and recursive cascaded network), the RCFRR‐Net achieved consistent and significant gains, which demonstrated that the proposed method had superior performance and generalization capability in medical image registration. The proposed RCFRR‐Net was a promising tool for various clinical applications.

Keywords

• 4D‐CT
• deformable image registration
• full‐resolution residual networks
• unsupervised learning