PET/CT 图像中卵巢癌 (OC) 病灶的准确分割对于有效的疾病管理至关重要，但放射组学分析的手动分割既费力又耗时。本研究介绍了 3D U-Net 深度学习模型的应用，利用先进的 3D 网络对 PET/CT 图像中的 OC 进行多类语义分割，并评估提取的放射组学特征的稳定性。利用来自 39 名 OC 患者的 3120 张 PET/CT 图像的数据集，该数据集分为训练 (70%)、验证 (15%) 和测试 (15%) 子集，以优化和评估模型的性能。 3D U-Net 模型，尤其是具有 VGG16 主干的模型，实现了显着的分割精度，Dice 得分为 0.74，精度为 0.76，召回率为 0.78。此外，该研究证明了放射组学特征的高度稳定性，超过 85% 的 PET 和 84% 的 CT 图像特征显示出高组内相关系数 (ICCs > 0.8)。这些结果强调了基于 3D U-Net 的自动化分割显着增强 OC 诊断和治疗计划的潜力。从自动分割中提取的放射组学特征的可靠性支持其在临床决策和个性化医疗中的应用。这项研究标志着肿瘤学诊断领域的重大进步，为分割 PET/CT 图像中的 OC 病变提供了一种稳健而有效的方法。通过解决手动分割的挑战并展示 3D 网络的有效性，这项研究为支持人工智能在提高肿瘤学诊断准确性和患者治疗效果方面的应用提供了越来越多的证据。© 2024。澳大利亚物理科学家和工程师学院在医学中。

Accurate segmentation of ovarian cancer (OC) lesions in PET/CT images is essential for effective disease management, yet manual segmentation for radiomics analysis is labor-intensive and time-consuming. This study introduces the application of a 3D U-Net deep learning model, leveraging advanced 3D networks, for multi-class semantic segmentation of OC in PET/CT images and assesses the stability of the extracted radiomics features. Utilizing a dataset of 3120 PET/CT images from 39 OC patients, the dataset was divided into training (70%), validation (15%), and test (15%) subsets to optimize and evaluate the model's performance. The 3D U-Net model, especially with a VGG16 backbone, achieved notable segmentation accuracy with a Dice score of 0.74, Precision of 0.76, and Recall of 0.78. Additionally, the study demonstrated high stability in radiomics features, with over 85% of PET and 84% of CT image features showing high intraclass correlation coefficients (ICCs > 0.8). These results underscore the potential of automated 3D U-Net-based segmentation to significantly enhance OC diagnosis and treatment planning. The reliability of the extracted radiomics features from automated segmentation supports its application in clinical decision-making and personalized medicine. This research marks a significant advancement in oncology diagnostics, providing a robust and efficient method for segmenting OC lesions in PET/CT images. By addressing the challenges of manual segmentation and demonstrating the effectiveness of 3D networks, this study contributes to the growing body of evidence supporting the application of artificial intelligence in improving diagnostic accuracy and patient outcomes in oncology.© 2024. Australasian College of Physical Scientists and Engineers in Medicine.