使用基于物理的数据增强的多任务3D CBCT到CT转换和危险器官分割
Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation
摘要：
在当前的临床实践中，噪声和伪影充斥的每周锥形束计算机断层扫描（CBCT）图像仅用于放疗期间的患者设置。治疗计划在治疗开始时使用高质量计划CT（pCT）图像和危险器官（OAR）结构的手动轮廓进行一次。如果每周CBCT图像的质量可以得到改善，同时分割OAR结构，这可以提供关键信息，用于调整放射治疗中期治疗以及导出治疗反应的生物标志物。使用一种新的基于物理学的数据增强策略，我们合成了一个大型数据集，该数据集包含局部晚期肺癌患者队列的完美/固有配准的计划CT和合成CBCT对，然后将其用于多任务3D深度学习框架，以同时分割并将真实的每周CBCT图像转换为高质量的计划CT图像。我们将模型生成的合成CT和OAR分割与真实计划CT和手动OAR分割进行了比较，并显示了有希望的结果。与pCT图像相比，真实第1周（基线）CBCT图像的平均MAE为162.77 HU，将其转换为合成CT图像，合成CT图像的平均MAE大幅改善，为29.31 HU，与pCT图像的平均结构相似性为92%。3D危险器官分割的平均DICE评分为：肺0.96、心脏0.88、脊髓0.83和食管0.66。这种方法可以让临床医生只使用常规的低质量CBCT图像来调整治疗计划，从而可能改善患者的预后。我们的代码，数据和预训练模型将通过我们基于物理的数据增强库Physics-ArX提供，网址为https://github.com/nadeemlab/Physics-ArX。
英文摘要：
In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response. Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered planning CT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask 3D deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality planning CT-like images. We compared the synthetic CT and OAR segmentations generated by the model to real planning CT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average MAE of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D organs-at-risk segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83 and esophagus 0.66. This approach could allow clinicians to adjust treatment plans using only the routine low-quality CBCT images, potentially improving patient outcomes. Our code, data, and pre-trained models will be made available via our physics-based data augmentation library, Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.
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