mlsanityData to 14 Oct 2026

TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

arXiv 2208.05868 ↗First posted 11 Aug 2022eess.IV cs.CVcited by 613

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Cited by 39 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 613 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2022.

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Citing papers per year: 2022: 1; 2023: 36; 2024: 146; 2025: 208; 2026: 222.

613 papers.

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  10. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 64
  11. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
  12. 2024TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRIcited by 59
38 more
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  6. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
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  16. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 29
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  31. 2024Is SAM 2 Better than SAM in Medical Image Segmentation?cited by 16
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