用AI生成更清晰的影像并自动分割，提升放疗中图像配准精度。
Improving Deformable Image Registration Accuracy through a Hybrid Similarity Metric and CycleGAN Based Auto-Segmentation
- 融合强度与结构信息的混合度量，提升配准稳定性。
- 前列腺配准DSC从0.61升至0.89，误差距离减半以上。
- 适合需要高精度图像配准的放疗临床与AI辅助研究者。
目的：变形图像配准（DIR）在自适应放疗（ART）中至关重要，用于应对解剖结构变化。传统基于强度的DIR方法在图像强度差异大时易失效。本研究评估一种结合强度与结构信息的混合相似性度量，通过基于CycleGAN的强度校正和自动分割，在三种DIR工作流中进行验证。方法：采用点到距离（PD）评分与强度相似性相结合的混合度量。使用2D CycleGAN模型在无配对的CT与CBCT图像上训练，生成合成CT（sCT）以增强软组织对比度。比较的工作流包括：(1) 传统基于强度的方法（无PD），(2) 在sCT上自动分割轮廓（CycleGAN PD），(3) 专家手动分割轮廓（Expert PD）。使用56例训练、14例验证的3D U-Net模型自动分割前列腺、膀胱和直肠。通过骰子相似系数（DSC）、95%豪斯多夫距离（HD）及定位器间距评估配准精度。结果：混合度量显著提升配准准确率。前列腺方面，DSC由0.61±0.18（无PD）提升至0.82±0.13（CycleGAN PD）和0.89±0.05（专家PD），95% HD从11.75 mm降至4.86 mm和3.27 mm；定位器间距由8.95 mm降至4.07 mm（CycleGAN PD）和4.11 mm（Expert PD）（p<0.05）。膀胱与直肠也观察到类似改进。结论：该研究证明，基于CycleGAN的自动分割与混合相似性度量可显著提升低对比度CBCT图像的配准精度。研究结果表明，将AI图像修复与分割技术融入放疗流程，有望提高治疗精准度并优化临床工作流。
原文摘要 · Abstract (English)
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.
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