This product includes software developed by 
- Jakob Wasserthal 
- Hanns-Christian Breit, 
- Manfred T. Meyer
- Maurice Pradella 
- Daniel Hinck
- Alexander W. Sauter
- Tobias Heye
- Daniel T. Boll
- Joshy Cyriac
- Shan Yang
- Michael Bach
- Martin Segeroth Author

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RIS CITATION
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TY  - JOUR
T1  - TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images
AU  - Wasserthal, Jakob
AU  - Breit, Hanns-Christian
AU  - Meyer, Manfred T.
AU  - Pradella, Maurice
AU  - Hinck, Daniel
AU  - Sauter, Alexander W.
AU  - Heye, Tobias
AU  - Boll, Daniel T.
AU  - Cyriac, Joshy
AU  - Yang, Shan
AU  - Bach, Michael
AU  - Segeroth, Martin
Y1  - 2023/07/05
PY  - 2023
DA  - 2023/09/01
N1  - doi: 10.1148/ryai.230024
DO  - 10.1148/ryai.230024
T2  - Radiology: Artificial Intelligence
JF  - Radiology: Artificial Intelligence
SP  - e230024
VL  - 5
IS  - 5
PB  - Radiological Society of North America
N2  - 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.
AB  - 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.
M3  - doi: 10.1148/ryai.230024
UR  - https://doi.org/10.1148/ryai.230024
Y2  - 2024/07/30
ER  - 


(https://github.com/wasserth/TotalSegmentator/blob/master/README.md)



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http://www.apache.org/licenses/LICENSE-2.0
