  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.
