Im Jung Ho, Lee Ik Jae, Choi Yeonho, Sung Jiwon, Ha Jin Sook, Lee Ho
CHA Bundang Medical Center, Department of Radiation Oncology, CHA University School of Medicine, Seongnam 13496, Korea.
Department of Radiation Oncology, Yonsei University College of Medicine, Seoul 03722, Korea.
Cancers (Basel). 2022 Jul 22;14(15):3581. doi: 10.3390/cancers14153581.
Objective: This study aimed to investigate the segmentation accuracy of organs at risk (OARs) when denoised computed tomography (CT) images are used as input data for a deep-learning-based auto-segmentation framework. Methods: We used non-contrast enhanced planning CT scans from 40 patients with breast cancer. The heart, lungs, esophagus, spinal cord, and liver were manually delineated by two experienced radiation oncologists in a double-blind manner. The denoised CT images were used as input data for the AccuContourTM segmentation software to increase the signal difference between structures of interest and unwanted noise in non-contrast CT. The accuracy of the segmentation was assessed using the Dice similarity coefficient (DSC), and the results were compared with those of conventional deep-learning-based auto-segmentation without denoising. Results: The average DSC outcomes were higher than 0.80 for all OARs except for the esophagus. AccuContourTM-based and denoising-based auto-segmentation demonstrated comparable performance for the lungs and spinal cord but showed limited performance for the esophagus. Denoising-based auto-segmentation for the liver was minimal but had statistically significantly better DSC than AccuContourTM-based auto-segmentation (p < 0.05). Conclusions: Denoising-based auto-segmentation demonstrated satisfactory performance in automatic liver segmentation from non-contrast enhanced CT scans. Further external validation studies with larger cohorts are needed to verify the usefulness of denoising-based auto-segmentation.
本研究旨在探讨当将去噪后的计算机断层扫描（CT）图像用作基于深度学习的自动分割框架的输入数据时，危及器官（OARs）的分割准确性。方法：我们使用了40例乳腺癌患者的非增强计划CT扫描图像。由两名经验丰富的放射肿瘤学家以双盲方式手动勾勒出心脏、肺、食管、脊髓和肝脏。将去噪后的CT图像用作AccuContourTM分割软件的输入数据，以增加非增强CT中感兴趣结构与不需要的噪声之间的信号差异。使用Dice相似系数（DSC）评估分割的准确性，并将结果与未进行去噪的传统基于深度学习的自动分割结果进行比较。结果：除食管外，所有OARs的平均DSC结果均高于0.80。基于AccuContourTM和基于去噪的自动分割在肺和脊髓方面表现相当，但在食管方面表现有限。基于去噪的肝脏自动分割效果最小，但DSC在统计学上显著优于基于AccuContourTM的自动分割（p < 0.05）。结论：基于去噪的自动分割在从非增强CT扫描中自动分割肝脏方面表现出令人满意的性能。需要进一步进行更大样本量的外部验证研究，以验证基于去噪的自动分割的实用性。
