■研究数据分布差异与联合深度学习(Fed-DL)算法在CT和MR图像上的肿瘤分割性能之间的相关性。
■回顾性收集了两个Fed-DL数据集(从2020年11月到2021年12月):一个肝肿瘤CT图像数据集(肝肿瘤分割联合成像[或,FILTS];三个站点,692次扫描)和一个公开可用的脑肿瘤MR图像数据集(联合肿瘤分割[或,FeTS];23个站点,1251次扫描)。来自两个数据集的扫描根据站点进行分组,肿瘤类型,肿瘤大小,数据集大小,和肿瘤强度。为了量化数据分布的差异,计算了以下四个距离度量:推土机距离(EMD),Bhattacharyya距离(BD),χ2距离(CSD),和Kolmogorov-Smirnov距离(KSD)。通过使用相同的分组数据集来训练联邦和集中式nnU-Net模型。Fed-DL模型的性能是通过使用Dice系数的比率来评估的,θ,在相同的80:20分割数据集上训练和测试的联合模型和集中模型之间。
■联合模型和集中模型之间的骰子系数比(θ)与数据分布之间的距离呈强烈负相关,EMD的相关系数为-0.920,-0.893用于BD,CSD为-0.899。然而,KSD与θ弱相关,相关系数为-0.479。
■Fed-DL模型在CT和MRI数据集上的肿瘤分割性能与数据分布之间的距离呈强烈负相关。关键词:CT,腹部/GI,肝脏,比较研究,MR成像,脑/脑干,卷积神经网络(CNN)联合深度学习,肿瘤分割,DataDistributionSupplementalmaterialisavailableforthisarticle.©RSNA,2023另见本期郭和白的评论。
UNASSIGNED: To investigate the correlation between differences in data distributions and federated deep learning (Fed-DL) algorithm performance in tumor segmentation on CT and MR images.
UNASSIGNED: Two Fed-DL datasets were retrospectively collected (from November 2020 to December 2021): one dataset of liver tumor CT images (Federated Imaging in Liver Tumor Segmentation [or, FILTS]; three sites, 692 scans) and one publicly available dataset of brain tumor MR images (Federated Tumor Segmentation [or, FeTS]; 23 sites, 1251 scans). Scans from both datasets were grouped according to site, tumor type, tumor size, dataset size, and tumor intensity. To quantify differences in data distributions, the following four distance metrics were calculated: earth mover\'s distance (EMD), Bhattacharyya distance (BD), χ2 distance (CSD), and Kolmogorov-Smirnov distance (KSD). Both federated and centralized nnU-Net models were trained by using the same grouped datasets. Fed-DL model performance was evaluated by using the ratio of Dice coefficients, θ, between federated and centralized models trained and tested on the same 80:20 split datasets.
UNASSIGNED: The Dice coefficient ratio (θ) between federated and centralized models was strongly negatively correlated with the distances between data distributions, with correlation coefficients of -0.920 for EMD, -0.893 for BD, and -0.899 for CSD. However, KSD was weakly correlated with θ, with a correlation coefficient of -0.479.
UNASSIGNED: Performance of Fed-DL models in tumor segmentation on CT and MRI datasets was strongly negatively correlated with the distances between data distributions.Keywords: CT, Abdomen/GI, Liver, Comparative Studies, MR Imaging, Brain/Brain Stem, Convolutional Neural Network (CNN), Federated Deep Learning, Tumor Segmentation, Data Distribution Supplemental material is available for this article. © RSNA, 2023See also the commentary by Kwak and Bai in this issue.