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
(this https URL) and open-source toolkit
(this https URL) are publicly available.

我们开发了一个开源且易于使用的分割模型，可以独立于 MR 序列，自动且鲁棒地对大多数主要解剖结构进行分割。