End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration

OBJECTIVE: To improve the efficiency of computed tomography (CT)‐magnetic resonance (MR) deformable image registration while ensuring the registration accuracy. METHODS: Two fully convolutional networks (FCNs) for generating spatial deformable grids were proposed using the Cycle‐Consistent method to ensure the deformed image consistency with the reference image data. In all, 74 pelvic cases consisting of both MR and CT images were studied, among which 64 cases were used as training data and 10 cases as the testing data. All training data were standardized and normalized, following simple image preparation to remove the redundant air. Dice coefficients and average surface distance (ASD) were calculated for regions of interest (ROI) of CT‐MR image pairs, before and after the registration. The performance of the proposed method (FCN with Cycle‐Consistent) was compared with that of Elastix software, MIM software, and FCN without cycle‐consistent. RESULTS: The results show that the proposed method achieved the best performance among the four registration methods tested in terms of registration accuracy and the method was more stable than others in general. In terms of average registration time, Elastix took 64 s, MIM software took 28 s, and the proposed method was found to be significantly faster, taking <0.1 s. CONCLUSION: The proposed method not only ensures the accuracy of deformable image registration but also greatly reduces the time required for image registration and improves the efficiency of the registration process. In addition, compared with other deep learning methods, the proposed method is completely unsupervised and end‐to‐end.