Improving Deformable Image Registration Accuracy through a Hybrid Similarity Metric and CycleGAN Based Auto-Segmentation

Abstract

Purpose: Deformable image registration (DIR) is critical in adaptive radiation therapy (ART) to account for anatomical changes. Conventional intensity-based DIR methods often fail when image intensities differ. This study evaluates a hybrid similarity metric combining intensity and structural information, leveraging CycleGAN-based intensity correction and auto-segmentation across three DIR workflows. Methods: A hybrid similarity metric combining a point-to-distance (PD) score and intensity similarity was implemented. Synthetic CT (sCT) images were generated using a 2D CycleGAN model trained on unpaired CT and CBCT images to enhance soft-tissue contrast. DIR workflows compared included: (1) traditional intensity-based (No PD), (2) auto-segmented contours on sCT (CycleGAN PD), and (3) expert manual contours (Expert PD). A 3D U-Net model trained on 56 images and validated on 14 cases segmented the prostate, bladder, and rectum. DIR accuracy was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and fiducial separation. Results: The hybrid metric improved DIR accuracy. For the prostate, DSC increased from 0.61+/-0.18 (No PD) to 0.82+/-0.13 (CycleGAN PD) and 0.89+/-0.05 (Expert PD), with reductions in 95% HD from 11.75 mm to 4.86 mm and 3.27 mm, respectively. Fiducial separation decreased from 8.95 mm to 4.07 mm (CycleGAN PD) and 4.11 mm (Expert PD) (p < 0.05). Improvements were also observed for the bladder and rectum. Conclusion: This study demonstrates that a hybrid similarity metric using CycleGAN-based auto-segmentation improves DIR accuracy, particularly for low-contrast CBCT images. These findings highlight the potential for integrating AI-based image correction and segmentation into ART workflows to enhance precision and streamline clinical processes.