We present a deep learning segmentation model that can automatically and robustly segment all major anatomical structures in body CT images. In this retrospective study, 1204 CT examinations (from the years 2012, 2016, and 2020) were used to segment 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age dependent volume and attenuation changes. The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 versus 0.871, respectively). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (e.g., age and aortic volume; age and mean attenuation of the autochthonous dorsal musculature). The developed model enables robust and accurate segmentation of 104 anatomical structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.

翻译：我们提出了一种深度学习分割模型，能够自动且鲁棒地分割人体CT图像中的所有主要解剖结构。在这项回顾性研究中，使用了1204次CT检查（来自2012年、2016年和2020年）来分割104个解剖结构（27个器官、59块骨骼、10块肌肉、8条血管），这些结构适用于器官体积测量、疾病特征描述以及手术或放疗计划等用例。CT图像从常规临床研究中随机采样，因此代表了真实世界数据集（包含不同年龄、病理、扫描仪、身体部位、序列和站点）。作者在该数据集上训练了nnU-Net分割算法，并计算了Dice相似系数（Dice）以评估模型性能。将训练好的算法应用于包含4004次全身CT检查的第二个数据集，以研究年龄相关的体积和衰减变化。所提出的模型在测试集上显示出高Dice得分（0.943），该测试集包含具有重大病理特征的广泛临床数据。该模型在另一个独立数据集上显著优于另一个公开可用的分割模型（Dice得分分别为0.932和0.871）。年龄研究显示，多种器官组群的年龄与体积及平均衰减之间存在显著相关性（例如年龄与主动脉体积；年龄与自体背侧肌肉的平均衰减）。开发的模型实现了对104个解剖结构的鲁棒且准确的分割。注释数据集（https://doi.org/10.5281/zenodo.6802613）和工具包（https://www.github.com/wasserth/TotalSegmentator）已公开提供。