TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images
Purpose: To develop an open-source and easy-to-use segmentation model that can automatically and robustly segment most major anatomical structures in MR images independently of the MR sequence. Materials and Methods: In this study we extended the capabilities of TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3 tissue types) relevant for use cases such as organ volumetry, disease characterization, and surgical planning. The MR and CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, contrasts, echo times, repetition times, field strengths, slice thicknesses and sites). We trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed two other publicly available segmentation models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542; p<0.001). On the CT image test set of the original TotalSegmentator paper it almost matches the performance of the original TotalSegmentator (Dice score, 0.960 versus 0.970; p<0.001). Conclusion: Our proposed model extends the capabilities of TotalSegmentator to MR images. The annotated dataset (https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.
View on arXiv@article{dántonoli2025_2405.19492,
title={ TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI },
author={ Tugba Akinci DÁntonoli and Lucas K. Berger and Ashraya K. Indrakanti and Nathan Vishwanathan and Jakob Weiß and Matthias Jung and Zeynep Berkarda and Alexander Rau and Marco Reisert and Thomas Küstner and Alexandra Walter and Elmar M. Merkle and Daniel Boll and Hanns-Christian Breit and Andrew Phillip Nicoli and Martin Segeroth and Joshy Cyriac and Shan Yang and Jakob Wasserthal },
journal={arXiv preprint arXiv:2405.19492},
year={ 2025 }
}