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Cited by 1,657Medical Image Segmentation TechniquesMedical Imaging and AnalysisArtificial Intelligence in Healthcare and Education

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

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Wasserthal, Jakob, Breit, Hanns-Christian, Meyer, Manfred T., Pradella, Maurice, Hinck, Daniel, Sauter, Alexander W., Heye, Tobias, Boll, Daniel T. +4 more·2023·Radiology: Artificial Intelligence
DOI 10.1148/ryai.230024PMC PMC10546353
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Abstract

Purpose To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images. Materials and Methods In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients 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. Results The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major abnormalities. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 vs 0.871; P < .001). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (eg, age and aortic volume [rs = 0.64; P < .001]; age and mean attenuation of the autochthonous dorsal musculature [rs = −0.74; P < .001]). Conclusion The developed model enables robust and accurate segmentation of 104 anatomic structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available. Keywords: CT, Segmentation, Neural Networks Supplemental material is available for this article. © RSNA, 2023 See also commentary by Sebro and Mongan in this issue.

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Cited by

Sorted by citations

Authors
Wasserthal, J., Breit, H.-C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., Heye, T., Boll, D. T., Cyriac, J., Yang, S., Bach, M., & Segeroth, M.
Affiliations
University Hospital of Basel (CH)
ORCID
Venue
Radiology: Artificial Intelligence
Publisher
Radiological Society of North America (RSNA)
Published
2023-09-01
Volume
5
Issue
5
Pages
e230024-e230024
ISSN
2638-6100 (ISSN-L)
DOI
10.1148/ryai.230024
PMC
PMC10546353
OpenAlex
W4383218413
Cited by
1,657
References
0
Citations by year
2026: 592 · 2025: 630 · 2024: 361 · 2023: 73 · 2022: 1
FWCI
150.30
Type
journal article
Language
en
Open access
Locations
  • Radiology Artificial Intelligence (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
TotalSegmentator, nnU-Net, surgical planning, radiotherapy treatment planning, deep learning segmentation

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