Shah Keyur D, Shackleford James A, Kandasamy Nagarajan, Sharp Gregory C
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104.
ArXiv. 2024 Nov 25:arXiv:2411.16992v1.
Deformable image registration (DIR) plays a critical role in adaptive radiation therapy (ART) to accommodate anatomical changes. However, conventional intensity-based DIR methods face challenges when registering images with unequal image intensities. In these cases, DIR accuracy can be improved using a hybrid image similarity metric which matches both image intensities and the location of known structures. This study aims to assess DIR accuracy using a hybrid similarity metric and leveraging CycleGAN-based intensity correction and auto-segmentation and comparing performance across three DIR workflows.
The proposed approach incorporates a hybrid image similarity metric combining a point-to-distance (PD) score and intensity similarity score. Synthetic CT (sCT) images were generated using a 2D CycleGAN model trained on unpaired CT and CBCT images, improving soft-tissue contrast in CBCT images. The performance of the approach was evaluated by comparing three DIR workflows: (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 was then trained on two datasets comprising 56 3D images and validated on 14 independent cases to segment the prostate, bladder, and rectum. DIR accuracy was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and fiducial separation metrics.
The hybrid similarity metric significantly 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 corresponding reductions in 95% HD from 11.75 mm to 4.86 mm and 3.27 mm, respectively. Fiducial separation was also reduced from 8.95 mm to 4.07 mm (CycleGAN PD) and 4.11 mm (Expert PD) (p < 0.05). Improvements in alignment were also observed for the bladder and rectum, highlighting the method's robustness.
A hybrid similarity metric that uses CycleGAN-based auto-segmentation presents a promising avenue for advancing DIR accuracy in ART. The study's findings suggest the potential for substantial enhancements in DIR accuracy by combining AI-based image correction and auto-segmentation with classical DIR.
可变形图像配准（DIR）在自适应放射治疗（ART）中起着关键作用，以适应解剖结构的变化。然而，传统的基于强度的DIR方法在配准图像强度不相等的图像时面临挑战。在这些情况下，可以使用一种混合图像相似性度量来提高DIR的准确性，该度量同时匹配图像强度和已知结构的位置。本研究旨在使用混合相似性度量并利用基于CycleGAN的强度校正和自动分割来评估DIR的准确性，并比较三种DIR工作流程的性能。
所提出的方法结合了一种混合图像相似性度量，该度量结合了点到距离（PD）分数和强度相似性分数。使用在未配对的CT和CBCT图像上训练的二维CycleGAN模型生成合成CT（sCT）图像，以改善CBCT图像中的软组织对比度。通过比较三种DIR工作流程来评估该方法的性能：（1）传统的基于强度的方法（无PD），（2）sCT上的自动分割轮廓（CycleGAN PD），以及（3）专家手动轮廓（专家PD）。然后在包含56幅三维图像的两个数据集上训练一个三维U-Net模型，并在14个独立病例上进行验证，以分割前列腺、膀胱和直肠。使用骰子相似系数（DSC）、95%豪斯多夫距离（HD）和基准分离度量来评估DIR的准确性。
混合相似性度量显著提高了DIR的准确性。对于前列腺，DSC从0.61±0.18（无PD）提高到0.82±0.13（CycleGAN PD）和0.89±0.05（专家PD），相应地，95%HD分别从11.75毫米降低到4.86毫米和3.27毫米。基准分离也从8.95毫米降低到4.07毫米（CycleGAN PD）和4.11毫米（专家PD）（p<0.05）。在膀胱和直肠的配准方面也观察到了改善，突出了该方法的稳健性。
使用基于CycleGAN的自动分割的混合相似性度量为提高ART中DIR的准确性提供了一条有前景的途径。该研究结果表明，通过将基于人工智能的图像校正和自动分割与经典DIR相结合，DIR准确性有大幅提高的潜力。
