Abstract
Stereotactic Body Radiation Therapy (SBRT) can be a precise, minimally invasive treatment method for liver cancer and liver metastases. However, the effectiveness of SBRT relies on the accurate delivery of the dose to the tumor while sparing healthy tissue. Challenges persist in ensuring breath-hold reproducibility, with current methods often requiring manual verification of liver dome positions from kV-triggered images. To address this, we propose a proof-of-principle study of a deep learning-based pipeline to automatically delineate the liver dome from kV-planar images. From 24 patients who received SBRT for liver cancer or metastasis inside liver, 711 KV-triggered images acquired for online breath-hold verification were included in the current study. We developed a pipeline comprising a trained U-Net for automatic liver dome region segmentation from the triggered images followed by extraction of the liver dome via thresholding, edge detection, and morphological operations. The performance and generalizability of the pipeline was evaluated using 2-fold cross validation. The training of the U-Net model for liver region segmentation took under 30 minutes and the automatic delineation of a liver dome for any triggered image took less than one second. The RMSE and rate of detection for Fold1 with 366 images was (6.4 +/- 1.6) mm and 91.7%, respectively. For Fold2 with 345 images, the RMSE and rate of detection was (7.7 +/- 2.3) mm and 76.3% respectively.
Abstract (translated)
立体定向体部放射治疗（SBRT）可以作为一种精确、微创的肝脏癌和肝转移瘤治疗方法。然而，SBRT的有效性依赖于准确地将剂量传递给肿瘤同时保护健康组织。确保呼吸保持可重复性的挑战仍然存在，当前的方法通常需要手动验证kV触发图像中的肝脏穹顶位置。为了解决这一问题，我们提出了一项基于深度学习的管道原理证明研究，以自动从kV平面图像中勾画肝脏穹顶区域。在本研究中，纳入了24名接受SBRT治疗肝癌或肝转移瘤患者的711张用于在线呼吸保持验证的KV触发图像。我们开发了一个流程，包括使用训练过的U-Net模型对触发图像中的肝脏区域进行自动分割，随后通过阈值处理、边缘检测和形态学操作提取肝脏穹顶。利用两折交叉验证评估了该流程的表现和泛化能力。用于肝脏区域分割的U-Net模型训练时间不到30分钟，任何触发图像的肝脏穹顶自动化勾画在1秒内完成。对于包含366张图像的第一折，RMSE（均方根误差）为(6.4 ± 1.6)毫米，检测率为91.7%；而对于包含345张图像的第二折，RMSE为(7.7 ± 2.3)毫米，检测率为76.3%。
URL
https://arxiv.org/abs/2411.15322
