TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

• URL: http://arxiv.org/abs/2208.05868v2
• Date: Fri, 16 Jun 2023 14:26:43 GMT
• Title: TotalSegmentator: robust segmentation of 104 anatomical structures in CT images
• Authors: Jakob Wasserthal and Hanns-Christian Breit and Manfred T. Meyer and Maurice Pradella and Daniel Hinck and Alexander W. Sauter and Tobias Heye and Daniel Boll and Joshy Cyriac and Shan Yang and Michael Bach and Martin Segeroth
• Abstract summary: We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
• Score: 48.50994220135258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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