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fhofmann/
BodyCompositionCT-ResEncM

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BodyCompositionCT-ResEncM

BodyCompositionCT-ResEncM is a 3D nnU-Net v2 model for segmenting native body-composition compartments and supporting anatomical structures in CT. This is a single-checkpoint ResEnc M model trained on the complete training set. For five-fold ResEnc L ensemble inference, see BodyCompositionCT-ResEncL.

Model overview

Property	Value
Task	Mutually exclusive 3D semantic segmentation
Input	One CT volume
Architecture	nnU-Net v2, `3d_fullres`, ResEnc M
Checkpoint	One `fold_all` model trained on all training cases
Training set	1,656 CT scans
Native output	Background plus seven foreground labels
License	CC BY 4.0

Native output labels

ID	Label	Description
0	Background
1	Muscle	Muscle compartment
2	Bone	Bone
3	Subcutaneous	Subcutaneous compartment
4	Abdominal	Abdominal compartment, with organs and larger vessels excluded
5	Thoracic	Thoracic compartment, with organs and larger vessels excluded
6	Heart	Heart
7	Lungs	Lungs

Labels 1, 3, 4, and 5 denote anatomical compartments rather than final attenuation-defined tissue masks. The muscle compartment can contain inter- and intramuscular connective tissue, smaller vessels, and larger fat-attenuation regions. IMAT is not a separate model class; CT-visible IMAT can be derived downstream within label 1 using HU-thresholding. All downstream tissue measurements should state their HU range, cleanup rules, and source compartment.

The machine-readable mapping is stored in dataset.json.

Training data and target construction

The training set contains 750 CT scans from SAROS and 906 from the TotalSegmentator v2 dataset. Another 332 cases (150 SAROS and 182 TotalSegmentator) were reserved and were not used for training.

Targets were constructed from dense BOA Task542 body-region predictions, sparse expert-reviewed SAROS labels, and TotalSegmentator-derived anatomical masks. Reviewed SAROS labels replaced predictions on annotated slices. TotalSegmentator muscle, bone, lung, tracheal, body-trunk, organ, vessel, and neural masks supplied corrections and exclusions. HU-guided candidate selection, morphological cleanup, model-assisted completion, and manual review produced the final seven foreground labels.

Only the open TotalSegmentator total and body tasks were used for target construction; the separately licensed tissue_types and vertebrae_body tasks were not used.

Training logs, plans, a configuration snapshot, and the training progress plot are included with the model.

Inference

The model uses the standard nnU-Net v2 results layout. The commands below were tested with nnU-Net v2 2.5.2. Input files must be unwindowed CT NIfTI volumes containing Hounsfield-unit values and named <case>_0000.nii.gz. Do not normalize or resample them manually; nnU-Net applies the preprocessing stored in the plans.

For integration with preprocessing and downstream tissue derivation, see the BodyComposition pipeline.

Set the nnU-Net results directory and download the model:

bash

export nnUNet_results="/path/to/nnUNet_results"

hf download fhofmann/BodyCompositionCT-ResEncM \
  --local-dir "${nnUNet_results}/Dataset611_Tissue"

Optionally verify the model artifacts:

bash

cd "${nnUNet_results}/Dataset611_Tissue"
sha256sum -c MODEL_ARTIFACTS.sha256

Run inference:

bash

nnUNetv2_predict \
  -d Dataset611_Tissue \
  -i INPUT_FOLDER \
  -o OUTPUT_FOLDER \
  -f all \
  -tr nnUNetTrainer \
  -c 3d_fullres \
  -p nnUNetResEncUNetMPlans

No independent nnU-Net postprocessing configuration was selected for this fold_all model. HU-based tissue derivation remains a separate downstream step.

The checkpoint is a serialized PyTorch artifact. Load it only from a trusted copy of this repository.

Evaluation status

The fold_all checkpoint was trained on all 1,656 training cases and therefore has no held-out training fold. Values in the training logs are optimization diagnostics, not independent performance estimates.

Intended use and limitations

The model is intended to provide compartment masks for research pipelines, including downstream body-composition measurements derived with CT attenuation thresholds.

It is not intended for diagnosis, treatment decisions, or stand-alone clinical measurement.
Training targets combine automated segmentations, deterministic corrections, and manual review; residual source-model and labeling errors can remain.
Performance may vary across populations, scanners, acquisition protocols, contrast phases, artifacts, implants, and uncommon anatomy.
The targets focus on the body trunk; extremity use is out of scope.
External validation is required for each intended population and measurement definition.

License and attribution

The trained weights, model documentation, metadata, and included training records are released under CC BY 4.0. Reuse requires appropriate credit, a link to the license, and an indication of changes. See LICENSE for the attribution format.

Training source data, source annotations, upstream checkpoints, and third-party software are not redistributed and retain their original terms:

Upstream material	Role	Terms
SAROS labels and split metadata	Sparse reviewed labels	CC BY 4.0
SAROS source CT scans	Training images	Collection-specific TCIA terms, including restricted, CC BY, and CC BY-NC collections
BOA Task542 weights	Dense initial body-region predictions	MIT
TotalSegmentator v2 dataset	Training CT scans and anatomical labels	CC BY 4.0
TotalSegmentator `total` and `body` tasks	Corrections and exclusions	Apache-2.0
nnU-Net v2	Training framework and architecture	Apache-2.0

See THIRD_PARTY_NOTICES.md for source links and the complete provenance and license summary.

References

Hofmann FO et al. Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies. Scientific Reports (2025). https://doi.org/10.1038/s41598-025-96238-6
Haubold J et al. SAROS: A dataset for whole-body region and organ segmentation in CT imaging. Scientific Data (2024). https://doi.org/10.1038/s41597-024-03337-6
Haubold J et al. BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care. Investigative Radiology (2023). https://doi.org/10.1097/RLI.0000000000001040
Wasserthal J et al. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence (2023). https://doi.org/10.1148/ryai.230024
Isensee F et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods (2021). https://doi.org/10.1038/s41592-020-01008-z
Isensee F et al. nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation. arXiv (2024). https://doi.org/10.48550/arXiv.2404.09556

Questions and feedback are welcome in the repository's Community section.