# TotalSegmentator
Tool for segmentation of most major anatomical structures in any CT or MR image. It was trained on a wide range of different CT and MR images (different scanners, institutions, protocols,...) and therefore works well on most images. A large part of the training dataset can be downloaded here: [CT dataset](https://doi.org/10.5281/zenodo.6802613) (1228 subjects) and [MR dataset](https://zenodo.org/doi/10.5281/zenodo.11367004) (616 subjects). You can also try the tool online at [totalsegmentator.com](https://totalsegmentator.com/) or as [3D Slicer extension](https://github.com/lassoan/SlicerTotalSegmentator).
**ANNOUNCEMENT: TotalSegmentator now can predict height, weight, age and sex: [totalseg_get_body_stats](#other-commands).**
**ANNOUNCEMENT: The new tasks `vertebrae_pp` will greatly improve vertebrae segmentations (less mixup of neighboring vertebrae).**
**ANNOUNCEMENT: We created web applications for [abdominal organ volume](https://compute.totalsegmentator.com/volume-report/), [aorta diameter](https://compute.totalsegmentator.com/aorta-report/), [spine report](https://compute.totalsegmentator.com/spine-report/), [pulmonary artery diameter](https://compute.totalsegmentator.com/pulmonary-report/), [contrast phase detection](https://compute.totalsegmentator.com/contrast-phase/) and [body weight prediction](https://compute.totalsegmentator.com/body-stats/).**
Main classes for CT and MR:
TotalSegmentator supports a lot more structures. See [subtasks](#subtasks) or [here](https://backend.totalsegmentator.com/find-task/) for more details.
Created by the department of [Research and Analysis at University Hospital Basel](https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin).
If you use it please cite our [Radiology AI paper](https://pubs.rsna.org/doi/10.1148/ryai.230024) ([free preprint](https://arxiv.org/abs/2208.05868)). If you use it for MR images please cite the [TotalSegmentator MRI Radiology paper](https://pubs.rsna.org/doi/10.1148/radiol.241613) ([free preprint](https://arxiv.org/abs/2405.19492)). Please also cite [nnUNet](https://github.com/MIC-DKFZ/nnUNet) since TotalSegmentator is heavily based on it.
### Installation
TotalSegmentator works on Ubuntu, Mac, and Windows and on CPU and GPU.
Install dependencies:
* Python >= 3.10
* [PyTorch](http://pytorch.org/) >= 2.0.0
Optionally:
* if you use the option `--preview` you have to install fury (`pip install fury`). FURY <2 additionally requires xvfb (`apt-get install xvfb`).
Install Totalsegmentator
```bash
pip install TotalSegmentator
```
### Usage
For CT images:
```bash
TotalSegmentator -i ct.nii.gz -o segmentations
```
For MR images:
```bash
TotalSegmentator -i mri.nii.gz -o segmentations --task total_mr
```
> Note: A Nifti file or a folder (or zip file) with all DICOM slices of one patient is allowed as input.
> Note: If you run on CPU use the option `--fast` (uses lower resolution) or `--roi_subset` to greatly improve runtime. If you run on a M-series Mac use `--device mps` for great speedup.
> Note: This is not a medical device and is not intended for clinical usage. However, it is part of several FDA-approved products, where it has been certified as a component of the overall system.
### Subtasks
Next to the default task (`total`) there are more subtasks with more classes. If the taskname ends with `_mr` it works for MR images, otherwise for CT images.
Openly available for any usage (Apache-2.0 license):
* **total**: default task containing 117 main classes (see [here](https://github.com/wasserth/TotalSegmentator#class-details) for a list of classes; see [here](resources/class_details.md) for more details)
* **total_mr**: default task containing 50 main classes on MR images (see [here](https://github.com/wasserth/TotalSegmentator#class-details) for a list of classes)
* **lung_vessels**: lung_arteries, lung_veins, lung_airways, lung_airways_wall (partly based on [paper](https://www.nature.com/articles/s41597-025-06074-6), also cite [paper](https://arxiv.org/abs/2404.03010))
* **lung_vessels_LEGACY**: lung_vessels, lung_trachea_bronchia (cite [paper](https://www.sciencedirect.com/science/article/pii/S0720048X22001097))
* **body**: body, body_trunc, body_extremities, skin
* **body_mr**: body_trunc, body_extremities (for MR images)
* **vertebrae_mr**: sacrum, vertebrae_L5, vertebrae_L4, vertebrae_L3, vertebrae_L2, vertebrae_L1, vertebrae_T12, vertebrae_T11, vertebrae_T10, vertebrae_T9, vertebrae_T8, vertebrae_T7, vertebrae_T6, vertebrae_T5, vertebrae_T4, vertebrae_T3, vertebrae_T2, vertebrae_T1, vertebrae_C7, vertebrae_C6, vertebrae_C5, vertebrae_C4, vertebrae_C3, vertebrae_C2, vertebrae_C1 (for CT this is part of the `total` task)
* **cerebral_bleed**: intracerebral_hemorrhage (cite [paper](https://www.mdpi.com/2077-0383/12/7/2631))*
* **hip_implant**: hip_implant* (download [training dataset](https://doi.org/10.5281/zenodo.20272030))
* **pleural_pericard_effusion**: pleural_effusion (cite [paper](http://dx.doi.org/10.1097/RLI.0000000000000869)), pericardial_effusion (cite [paper](http://dx.doi.org/10.3390/diagnostics12051045), download [training dataset](https://doi.org/10.5281/zenodo.20272294))*
* **head_glands_cavities**: eye_left, eye_right, eye_lens_left, eye_lens_right, optic_nerve_left, optic_nerve_right, parotid_gland_left, parotid_gland_right, submandibular_gland_right, submandibular_gland_left, nasopharynx, oropharynx, hypopharynx, nasal_cavity_right, nasal_cavity_left, auditory_canal_right, auditory_canal_left, soft_palate, hard_palate (cite [paper](https://www.mdpi.com/2072-6694/16/2/415))
* **head_muscles**: masseter_right, masseter_left, temporalis_right, temporalis_left, lateral_pterygoid_right, lateral_pterygoid_left, medial_pterygoid_right, medial_pterygoid_left, tongue, digastric_right, digastric_left
* **headneck_bones_vessels**: larynx_air, thyroid_cartilage, hyoid, cricoid_cartilage, zygomatic_arch_right, zygomatic_arch_left, styloid_process_right, styloid_process_left, internal_carotid_artery_right, internal_carotid_artery_left, internal_jugular_vein_right, internal_jugular_vein_left (cite [paper](https://www.mdpi.com/2072-6694/16/2/415))
* **headneck_muscles**: sternocleidomastoid_right, sternocleidomastoid_left, superior_pharyngeal_constrictor, middle_pharyngeal_constrictor, inferior_pharyngeal_constrictor, trapezius_right, trapezius_left, platysma_right, platysma_left, levator_scapulae_right, levator_scapulae_left, anterior_scalene_right, anterior_scalene_left, middle_scalene_right, middle_scalene_left, posterior_scalene_right, posterior_scalene_left, sterno_thyroid_right, sterno_thyroid_left, thyrohyoid_right, thyrohyoid_left, prevertebral_right, prevertebral_left (cite [paper](https://www.mdpi.com/2072-6694/16/2/415))
* **liver_vessels**: liver_vessels, liver_tumor (cite [paper](https://arxiv.org/abs/1902.09063))*
* **oculomotor_muscles**: skull, eyeball_right, lateral_rectus_muscle_right, superior_oblique_muscle_right, levator_palpebrae_superioris_right, superior_rectus_muscle_right, medial_rectus_muscle_left, inferior_oblique_muscle_right, inferior_rectus_muscle_right, optic_nerve_left, eyeball_left, lateral_rectus_muscle_left, superior_oblique_muscle_left, levator_palpebrae_superioris_left, superior_rectus_muscle_left, medial_rectus_muscle_right, inferior_oblique_muscle_left, inferior_rectus_muscle_left, optic_nerve_right*
* **lung_nodules**: lung, lung_nodules (provided by [BLUEMIND AI](https://bluemind.co/): Fitzjalen R., Aladin M., Nanyan G.) (trained on 1353 subjects, partly from LIDC-IDRI)
* **kidney_cysts**: kidney_cyst_left, kidney_cyst_right (strongly improved accuracy compared to kidney_cysts inside of `total` task)
* **breasts**: breast
* **liver_segments**: liver_segment_1, liver_segment_2, liver_segment_3, liver_segment_4, liver_segment_5, liver_segment_6, liver_segment_7, liver_segment_8 (Couinaud segments) (cite [paper](https://doi.org/10.1007/978-3-030-32692-0_32))*
* **liver_segments_mr**: liver_segment_1, liver_segment_2, liver_segment_3, liver_segment_4, liver_segment_5, liver_segment_6, liver_segment_7, liver_segment_8 (for MR images) (Couinaud segments) (download [training dataset](https://doi.org/10.5281/zenodo.20272491))*
* **craniofacial_structures**: mandible, teeth_lower, skull, head, sinus_maxillary, sinus_frontal, teeth_upper (cite [paper](https://www.ijoms.com/article/S0901-5027(25)01499-7/fulltext))
* **abdominal_muscles**: pectoralis_major_right, pectoralis_major_left, rectus_abdominis_right, rectus_abdominis_left, serratus_anterior_right, serratus_anterior_left, latissimus_dorsi_right, latissimus_dorsi_left, trapezius_right, trapezius_left, external_oblique_right, external_oblique_left, internal_oblique_right, internal_oblique_left, erector_spinae_right, erector_spinae_left, transversospinalis_right, transversospinalis_left, psoas_major_right, psoas_major_left, quadratus_lumborum_right, quadratus_lumborum_left (cite [paper](https://doi.org/10.1101/2025.01.13.25319967)) (only segments within T4-L4)*
* **teeth**: "lower_jawbone", "upper_jawbone", "left_inferior_alveolar_canal", "right_inferior_alveolar_canal", "left_maxillary_sinus", "right_maxillary_sinus", "pharynx", "bridge", "crown", "implant", "upper_right_central_incisor_fdi11", "upper_right_lateral_incisor_fdi12", "upper_right_canine_fdi13", "upper_right_first_premolar_fdi14", "upper_right_second_premolar_fdi15", "upper_right_first_molar_fdi16", "upper_right_second_molar_fdi17", "upper_right_third_molar_fdi18", "upper_left_central_incisor_fdi21", "upper_left_lateral_incisor_fdi22", "upper_left_canine_fdi23", "upper_left_first_premolar_fdi24", "upper_left_second_premolar_fdi25", "upper_left_first_molar_fdi26", "upper_left_second_molar_fdi27", "upper_left_third_molar_fdi28", "lower_left_central_incisor_fdi31", "lower_left_lateral_incisor_fdi32", "lower_left_canine_fdi33", "lower_left_first_premolar_fdi34", "lower_left_second_premolar_fdi35", "lower_left_first_molar_fdi36", "lower_left_second_molar_fdi37", "lower_left_third_molar_fdi38", "lower_right_central_incisor_fdi41", "lower_right_lateral_incisor_fdi42", "lower_right_canine_fdi43", "lower_right_first_premolar_fdi44", "lower_right_second_premolar_fdi45", "lower_right_first_molar_fdi46", "lower_right_second_molar_fdi47", "lower_right_third_molar_fdi48", "left_mandibular_incisive_canal_fdi103", "right_mandibular_incisive_canal_fdi104", "lingual_canal", "upper_right_central_incisor_pulp_fdi111", "upper_right_lateral_incisor_pulp_fdi112", "upper_right_canine_pulp_fdi113", "upper_right_first_premolar_pulp_fdi114", "upper_right_second_premolar_pulp_fdi115", "upper_right_first_molar_pulp_fdi116", "upper_right_second_molar_pulp_fdi117", "upper_right_third_molar_pulp_fdi118", "upper_left_central_incisor_pulp_fdi121", "upper_left_lateral_incisor_pulp_fdi122", "upper_left_canine_pulp_fdi123", "upper_left_first_premolar_pulp_fdi124", "upper_left_second_premolar_pulp_fdi125", "upper_left_first_molar_pulp_fdi126", "upper_left_second_molar_pulp_fdi127", "upper_left_third_molar_pulp_fdi128", "lower_left_central_incisor_pulp_fdi131", "lower_left_lateral_incisor_pulp_fdi132", "lower_left_canine_pulp_fdi133", "lower_left_first_premolar_pulp_fdi134", "lower_left_second_premolar_pulp_fdi135", "lower_left_first_molar_pulp_fdi136", "lower_left_second_molar_pulp_fdi137", "lower_left_third_molar_pulp_fdi138", "lower_right_central_incisor_pulp_fdi141", "lower_right_lateral_incisor_pulp_fdi142", "lower_right_canine_pulp_fdi143", "lower_right_first_premolar_pulp_fdi144", "lower_right_second_premolar_pulp_fdi145", "lower_right_first_molar_pulp_fdi146", "lower_right_second_molar_pulp_fdi147", "lower_right_third_molar_pulp_fdi148" (based on the ToothFairy3 dataset, cite [paper](https://openaccess.thecvf.com/content/CVPR2025/html/Bolelli_Segmenting_Maxillofacial_Structures_in_CBCT_Volumes_CVPR_2025_paper.html))
* **trunk_cavities**: abdominal_cavity, thoracic_cavity, pericardium, mediastinum
* **ventricle_parts**: ventricle_frontal_horn_left, ventricle_occipital_horn_left, ventricle_body_left, ventricle_temporal_horn_left, ventricle_trigone_left, ventricle_frontal_horn_right, ventricle_occipital_horn_right, ventricle_body_right, ventricle_temporal_horn_right, ventricle_trigone_right, third_ventricle, fourth_ventricle
* **liver_lesions**: liver_lesions (cite [paper](https://doi.org/10.1007/s10278-025-01716-y), download [training dataset](https://doi.org/10.5281/zenodo.20272571))*
* **liver_lesions_mr**: liver_lesions (for MR images) (cite [paper](https://doi.org/10.1007/s10278-025-01716-y), download [training dataset](https://doi.org/10.5281/zenodo.20272347))*
* **vertebrae_pp**: vertebral bodies C1-L5 with per-vertebra labels (a lot less segmentation errors than vertebrae segmentations from `total` task because of added postprocessing)
* **vertebrae_pp_refined**: vertebral bodies C1-L5 with per-vertebra labels, refined with the `vertebrae_body` mask for sharper borders (borders are more accurate than in `vertebrae_pp` but has longer runtime)
* **vertebrae_body**: vertebral body of all vertebrae (without the vertebral arch), intervertebral_discs (for MR this is part of the `total_mr` task)
*: These models are not trained on the full totalsegmentator dataset but on some small other datasets. Therefore, expect them to work less robustly.
Available with a license (free licenses available for non-commercial usage [here](https://backend.totalsegmentator.com/license-academic/). For a commercial license contact jakob.wasserthal@usb.ch):
* **heartchambers_highres**: myocardium, atrium_left, ventricle_left, atrium_right, ventricle_right, aorta, pulmonary_artery (trained on sub-millimeter resolution; [details](resources/heartchambers_highres_details.md))
* **appendicular_bones**: patella, tibia, fibula, tarsal, metatarsal, phalanges_feet, ulna, radius, carpal, metacarpal, phalanges_hand
* **appendicular_bones_mr**: patella, tibia, fibula, tarsal, metatarsal, phalanges_feet, ulna, radius (for MR images)
* **tissue_types**: subcutaneous_fat, torso_fat, skeletal_muscle
* **tissue_types_mr**: subcutaneous_fat, torso_fat, skeletal_muscle (for MR images; works on all sequences but for DIXON prefer F for subcut./torso fat and W for muscle as input)
* **tissue_4_types**: subcutaneous_fat, torso_fat, skeletal_muscle, intermuscular_fat (in contrast to `tissue_types` skeletal_muscle is split into two classes: muscle and fat) (see [more details](#postprocessing))
* **brain_structures**: brainstem, subarachnoid_space, venous_sinuses, septum_pellucidum, cerebellum, caudate_nucleus, lentiform_nucleus, insular_cortex, internal_capsule, ventricle, central_sulcus, frontal_lobe, parietal_lobe, occipital_lobe, temporal_lobe, thalamus (NOTE: this is for CT) (cite [paper](https://doi.org/10.1148/ryai.2020190183) as our model is partly based on this)
* **face**: face_region (for anonymization)
* **face_mr**: face_region (for anonymization)
* **thigh_shoulder_muscles**: quadriceps_femoris_left, quadriceps_femoris_right, thigh_medial_compartment_left, thigh_medial_compartment_right, thigh_posterior_compartment_left, thigh_posterior_compartment_right, sartorius_left, sartorius_right, deltoid, supraspinatus, infraspinatus, subscapularis, coracobrachial, trapezius, pectoralis_minor, serratus_anterior, teres_major, triceps_brachii
* **thigh_shoulder_muscles_mr**: quadriceps_femoris_left, quadriceps_femoris_right, thigh_medial_compartment_left, thigh_medial_compartment_right, thigh_posterior_compartment_left, thigh_posterior_compartment_right, sartorius_left, sartorius_right, deltoid, supraspinatus, infraspinatus, subscapularis, coracobrachial, trapezius, pectoralis_minor, serratus_anterior, teres_major, triceps_brachii (for MR images)
* **coronary_arteries**: coronary_arteries (also works on non-contrast images)
* **coronary_arteries_LEGACY**: coronary_arteries (previous version of the coronary_arteries task used until v2.12.0)
* **aortic_sinuses**: left_ventricular_outflow_tract, right_coronary_cusp, left_coronary_cusp, non_coronary_cusp
* **brain_aneurysm**: aneurysm (only works with TOF MRI images; [model details](https://doi.org/10.5281/zenodo.17894703)) (CC BY-NC 4.0 license, no commercial license available) (cite [paper](https://doi.org/10.1007/s10278-025-01533-3))
Usage:
```bash
TotalSegmentator -i ct.nii.gz -o segmentations -ta <task_name>
```
Confused by all the structures and tasks? Check [this](https://backend.totalsegmentator.com/find-task/) to search through available structures and tasks or run `totalseg_info`.
The mapping from label ID to class name can be found [here](https://github.com/wasserth/TotalSegmentator/blob/master/totalsegmentator/map_to_binary.py).
If you have a nnU-Net model for some structures not supported yet, you can contribute it. This will enable all TotalSegmentator users to easily use it and at the same time increase the reach of your work by more people citing your paper. Contact jakob.wasserthal@usb.ch.
Thank you to [INGEDATA](https://www.ingedata.ai/) for providing a team of radiologists to support some of the data annotations.
### Advanced settings
* `--device`: Choose `cpu` or `gpu` or `gpu:X`
* `--fast`: For faster runtime and less memory requirements use this option. It will run a lower resolution model (3mm instead of 1.5mm).
* `--roi_subset`: Takes a space-separated list of class names (e.g. `spleen colon brain`) and only predicts those classes. Saves a lot of runtime and memory. Might be less accurate especially for small classes (e.g. prostate).
* `--ml`: This will save one nifti file containing all labels instead of one file for each class. Saves runtime during saving of nifti files. (see [here](https://github.com/wasserth/TotalSegmentator#class-details) for index to class name mapping).
* `--output_type`: This will output the segmentation as DICOM. Supported are `dicom_seg` requires (`pip install highdicom`) and `dicom_rtstruct` requires (`pip install rt_utils`).
* `--statistics`: This will generate a file `statistics.json` with volume (in mm³) and mean intensity of each class. Incomplete structures receive zero by default (see [Typical problems](#typical-problems)).
* `--statistics_extra`: In addition to volume and intensity, also compute `n_voxels`, intensity std/min/max and the morphometric `centroid_vox` and `bbox_vox` (voxel coordinates) for each class. Off by default to keep the statistics runtime unchanged.
* `--higher_order_resampling`: Uses higher order upsampling of the segmentations. Smoother (especially for `--fast`) but slower.
* `--resampling_order`: Spline interpolation order for input image resampling (default: 1). Setting this to 3 may give slightly better segmentation accuracy at the cost of slower resampling.
* `--save_lowres`: Save the segmentation at the model resolution (e.g. 1.5mm, or 3mm/6mm with `--fast`/`--fastest`) instead of upsampling it back to the input resolution. Skips the slow upsample and keeps the output on the model grid.
* `--robust_crop`: For some tasks and for roi_subset a 6mm low resolution model is used to crop to the region of interest. Sometimes this model is incorrect, which leads to artifacts like segmentations being cut off. robust_crop will use a better but slower 3mm model instead.
* `--preview`: This will generate a 3D rendering of all classes, giving you a quick overview if the segmentation worked and where it failed (see `preview.png` in output directory).
* `--radiomics`: This will generate a file `statistics_radiomics.json` with the radiomics features of each class. You have to install pyradiomics to use this (`pip install pyradiomics`).
* `--report`: This will write a machine-readable JSON run manifest to the given path (software and model versions, resolved device, task, classes, runtime and the list of output files). Useful for reproducible pipelines and automation.
* `--list-tasks` / `--list-classes [task]`: Print the available tasks (or the classes of one task) and exit, without running a segmentation. For machine-readable output use the `totalseg_info` command below.
### Other commands
If you want to know body weight, size, age, sex, BMI, BSA, image noise, contrast, FOV and more information about the image you can use the following command (requires `pip install timm monai`). It runs on CPU in <1min. It requires a license which you can get for free for non-commercial usage [here](https://backend.totalsegmentator.com/license-academic/). More details can be found [here](resources/body_stats_prediction.md) or in the [paper](https://arxiv.org/abs/2608.29348):
```bash
totalseg_get_body_stats -i ct.nii.gz -o body_stats.json -m [ct/mr]
```
If you want to know which contrast phase a CT image is you can use the following command (requires `pip install xgboost`). More details can be found [here](resources/contrast_phase_prediction.md):
```bash
totalseg_get_phase -i ct.nii.gz -o contrast_phase.json
```
If you want to know which modality (CT or MR) an image is you can use the following command (requires `pip install xgboost`).
```bash
totalseg_get_modality -i image.nii.gz -o modality.json
```
If you want to combine some subclasses (e.g. lung lobes) into one binary mask (e.g. entire lung) you can use the following command:
```bash
totalseg_combine_masks -i totalsegmentator_output_dir -o combined_mask.nii.gz -m lung
```
If you want to calculate the [Evans index](https://radiopaedia.org/articles/evans-index-2) you can use the following command:
```bash
totalseg_evans_index -i ct_skull.nii.gz -o evans_index.json -p evans_index.png
```
If you want to find osteoporotic spine fractures you can use the following command. Please cite this [paper](https://rdcu.be/fp2jd):
```bash
totalseg_spine_report.py -i ct.nii.gz -o spine_report.nii.gz -j spine_report.json -l spine_report.log
```
To measure aortic landmark diameters and section lengths/volumes (and curved planar reformation (CPR)), use the aorta report command. (requires a license which you can get for free for non-commercial usage [here](https://backend.totalsegmentator.com/license-academic/)). See the [aorta report documentation](resources/aorta_report.md) for more details:
```bash
totalseg_aorta_report -i ct.nii.gz \
-o aorta_report.nii.gz -j aorta_report.json -l aorta_report.log \
-c aorta_cpr.png --run_models
```
To measure the proximal main pulmonary artery, sinotubular junction,
bifurcation, and the left and right pulmonary arteries, use the pulmonary
artery report command. See the
[pulmonary report documentation](resources/pulmonary_report.md):
```bash
totalseg_pulmonary_report -i ct.nii.gz \
-o pulmonary_report.nii.gz -j pulmonary_report.json \
-l pulmonary_report.log --run_models
```
Normally weights are automatically downloaded when running TotalSegmentator. If you want to download the weights with an extra command (e.g. when building a docker container) use this:
```bash
totalseg_download_weights -t <task_name>
```
This will download them to `~/.totalsegmentator/nnunet/results`. You can change this path by doing `export TOTALSEG_HOME_DIR=/new/path/.totalsegmentator`. If your machine has no internet, then download on another machine with internet and copy `~/.totalsegmentator` to the machine without internet.
After acquiring a license number for the non-open tasks you can set it with the following command:
```bash
totalseg_set_license -l aca_12345678910
```
If you want to run as fast as possible to process many cases and lower resolution is ok, I would recommend this setting (for a large CT image this takes ~30s on GPU and ~70s on CPU):
```bash
TotalSegmentator -i ct.nii.gz -o seg.nii.gz -ml -f -sl -ro 1
```
You can output the softmax probabilities. This will give you a `.npz` file you can load with numpy. The geometry
might not be identical to your input image. There will also be a `.pkl` output file with geometry
information. This does not work well for the `total` task since this is based on multiple models.
```bash
TotalSegmentator -i ct.nii.gz -o seg -ta lung_nodules --save_probabilities probs.npz
```
If you do not have internet access on the machine you want to run TotalSegmentator on:
1. Install TotalSegmentator [and set up the license] on a machine with internet.
2. Run TotalSegmentator for one subject on this machine. This will download the weights and save them to `~/.totalsegmentator`.
3. Copy the folder `~/.totalsegmentator` from this machine to the machine without internet.
4. TotalSegmentator should now work also on the machine without internet.
If you want to discover which tasks are available and which classes each one outputs (e.g. to find valid `--roi_subset` names) you can use the `totalseg_info` command. It runs instantly, needs no GPU and downloads no model weights, which also makes it convenient for scripts and AI coding agents:
```bash
totalseg_info --list-tasks # table of tasks (modality, license, number of classes)
totalseg_info --classes -ta total # class index -> name for one task
totalseg_info --json # full capability registry as JSON
```
### MCP server
TotalSegmentator provides a Model Context Protocol (MCP) server for AI clients. See the [MCP server documentation](totalsegmentator_mcp/README.md) for stdio, HTTP, Claude Code, and Cursor instructions.
```bash
pipx install \
'TotalSegmentator[mcp] @ git+https://github.com/wasserth/TotalSegmentator.git'
```
### Web applications
We provide the following web applications to easily process your images:
* [TotalSegmentator](https://totalsegmentator.com/): Run totalsegmentator on your own images via a simple web interface.
* [Volume Report](https://compute.totalsegmentator.com/volume-report/): Get the volume of abdominal organs + tissue und bone density. Also show percentile in population.
* [Evans Index](https://compute.totalsegmentator.com/evans-index/): Compute the Evans index.
* [Aorta Report](https://compute.totalsegmentator.com/aorta-report/): Analyse the diameter along the aorta.
* [Body Stats](https://compute.totalsegmentator.com/body-stats/): Predict body stats like weight, size, age, sex, BMI and BSA from CT or MR scans.
### Run via docker
We also provide a docker container which can be used the following way
```bash
docker run --gpus 'device=0' --shm-size=16G -v /absolute/path/to/my/data/directory:/tmp wasserth/totalsegmentator:2.11.0 TotalSegmentator -i /tmp/ct.nii.gz -o /tmp/segmentations
```
### Resource Requirements
Totalsegmentator has the following runtime and memory requirements (using an Nvidia RTX 3090 GPU):
(1.5mm is the normal model and 3mm is the `--fast` model. With v2 the runtimes have increased a bit since
we added more classes.)
If you want to reduce memory consumption you can use the following options:
* `--fast`: This will use a lower-resolution model
* `--body_seg`: This will crop the image to the body region before processing it
* `--roi_subset <list of classes>`: This will only predict a subset of classes
* `--force_split`: This will split the image into 3 parts and process them one after another. (Do not use this for small images. Splitting these into even smaller images will result in a field of view which is too small.)
* `--nr_thr_saving 1`: Saving big images with several threads will take a lot of memory
### Python API
You can run totalsegmentator via Python:
```python
import nibabel as nib
from totalsegmentator.python_api import totalsegmentator
if __name__ == "__main__":
# option 1: provide input and output as file paths
totalsegmentator(input_path, output_path)
# option 2: provide input and output as nifti image objects
input_img = nib.load(input_path)
output_img = totalsegmentator(input_img)
nib.save(output_img, output_path)
```
You can see all available arguments [here](https://github.com/wasserth/TotalSegmentator/blob/master/totalsegmentator/python_api.py). Running from within the main environment should avoid some multiprocessing issues.
The segmentation image contains the names of the classes in the extended header. If you want to load this additional header information you can use the following code (requires `pip install xmltodict`):
```python
from totalsegmentator.nifti_ext_header import load_multilabel_nifti
segmentation_nifti_img, label_map_dict = load_multilabel_nifti(image_path)
```
### Install latest master branch (contains latest bug fixes)
```bash
pip install git+https://github.com/wasserth/TotalSegmentator.git
```
### Train/validation/test split
The exact split of the dataset can be found in the file `meta.csv` inside of the [dataset](https://doi.org/10.5281/zenodo.6802613). This was used for the validation in our paper.
The exact numbers of the results for the high-resolution model (1.5mm) can be found [here](resources/results_all_classes_v1.json). The paper shows these numbers in the supplementary materials Figure 11.
### Retrain model and run evaluation
See [here](resources/train_nnunet.md) for more info on how to train a nnU-Net yourself on the TotalSegmentator dataset, how to split the data into train/validation/test set as in our paper, and how to run the same evaluation as in our paper.
### Postprocessing
In some cases the following kind of manual postprocessing might be useful:
* `tissue_4_types`: Within the `skeletal_muscle` class threshold -190 to -30 HU and move this to `intermuscular_fat` class. This gives a more detailed segmentation of the intermuscular fat.
### Typical problems
**Zero volume and intensity despite a nonempty segmentation**
By default, `--statistics` sets a structure's `volume` and `intensity` to zero if its mask reaches the image border region. These structures are treated as potentially incomplete.
To calculate statistics for these structures as well, use:
```bash
TotalSegmentator -i ct.nii.gz -o seg --statistics --stats_include_incomplete
```
For the Python API use `statistics_exclude_masks_at_border=False`.
**ITK loading Error**
When you get the following error message
```text
ITK ERROR: ITK only supports orthonormal direction cosines. No orthonormal definition was found!
```
you should do
```bash
pip install SimpleITK==2.0.2
```
Alternatively you can try
```bash
fslorient -copysform2qform input_file
[fslreorient2std input_file output_file]
```
or use [this python command](https://github.com/MIC-DKFZ/nnDetection/issues/24#issuecomment-2627684467).
**Bad segmentations**
When you get bad segmentation results check the following:
* does your input image contain the original HU values or are the intensity values rescaled to a different range?
* is the patient normally positioned in the image? (In axial view is the spine at the bottom of the image? In the coronal view is the head at the top of the image?)
### Running v1
If you want to keep on using TotalSegmentator v1 (e.g. because you do not want to change your pipeline) you
can install it with the following command:
```bash
pip install TotalSegmentator==1.5.7
```
The documentation for v1 can be found [here](https://github.com/wasserth/TotalSegmentator/tree/v1.5.7). Bugfixes for v1 are developed in the branch `v1_bugfixes`.
Our Radiology AI publication refers to TotalSegmentator v1.
### Other
* TotalSegmentator sends anonymous usage statistics to help us improve it further. You can deactivate it by setting `send_usage_stats` to `false` in `~/.totalsegmentator/config.json`.
* At [changes and improvements](resources/improvements_in_v2.md) you can see an overview of differences between v1 and v2.
### Reference
For more details see our [Radiology AI paper](https://pubs.rsna.org/doi/10.1148/ryai.230024) ([freely available preprint](https://arxiv.org/abs/2208.05868)).
If you use this tool please cite it as follows
```text
Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024
```
Please also cite [nnUNet](https://github.com/MIC-DKFZ/nnUNet) since TotalSegmentator is heavily based on it.
Moreover, we would really appreciate it if you let us know what you are using this tool for. You can also tell us what classes we should add in future releases. You can do so [here](https://github.com/wasserth/TotalSegmentator/issues/1).
### Class details
The following table shows a list of all classes for task `total`.
TA2 is a standardized way to name anatomy. Mostly the TotalSegmentator names follow this standard.
For some classes they differ which you can see in the table below.
[Here](totalsegmentator/resources/totalsegmentator_snomed_mapping.csv) you can find a mapping of the TotalSegmentator classes to SNOMED-CT codes.
| Index | TotalSegmentator name | TA2 name |
| ----: | -------------------------------- | --------------------------- |
| 1 | spleen | |
| 2 | kidney\_right | |
| 3 | kidney\_left | |
| 4 | gallbladder | |
| 5 | liver | |
| 6 | stomach | |
| 7 | pancreas | |
| 8 | adrenal\_gland\_right | suprarenal gland |
| 9 | adrenal\_gland\_left | suprarenal gland |
| 10 | lung\_upper\_lobe\_left | superior lobe of left lung |
| 11 | lung\_lower\_lobe\_left | inferior lobe of left lung |
| 12 | lung\_upper\_lobe\_right | superior lobe of right lung |
| 13 | lung\_middle\_lobe\_right | middle lobe of right lung |
| 14 | lung\_lower\_lobe\_right | inferior lobe of right lung |
| 15 | esophagus | |
| 16 | trachea | |
| 17 | thyroid\_gland | |
| 18 | small\_bowel | small intestine |
| 19 | duodenum | |
| 20 | colon | |
| 21 | urinary\_bladder | |
| 22 | prostate | |
| 23 | kidney\_cyst\_left | |
| 24 | kidney\_cyst\_right | |
| 25 | sacrum | |
| 26 | vertebrae\_S1 | |
| 27 | vertebrae\_L5 | |
| 28 | vertebrae\_L4 | |
| 29 | vertebrae\_L3 | |
| 30 | vertebrae\_L2 | |
| 31 | vertebrae\_L1 | |
| 32 | vertebrae\_T12 | |
| 33 | vertebrae\_T11 | |
| 34 | vertebrae\_T10 | |
| 35 | vertebrae\_T9 | |
| 36 | vertebrae\_T8 | |
| 37 | vertebrae\_T7 | |
| 38 | vertebrae\_T6 | |
| 39 | vertebrae\_T5 | |
| 40 | vertebrae\_T4 | |
| 41 | vertebrae\_T3 | |
| 42 | vertebrae\_T2 | |
| 43 | vertebrae\_T1 | |
| 44 | vertebrae\_C7 | |
| 45 | vertebrae\_C6 | |
| 46 | vertebrae\_C5 | |
| 47 | vertebrae\_C4 | |
| 48 | vertebrae\_C3 | |
| 49 | vertebrae\_C2 | |
| 50 | vertebrae\_C1 | |
| 51 | heart | |
| 52 | aorta | |
| 53 | pulmonary\_vein | |
| 54 | brachiocephalic\_trunk | |
| 55 | subclavian\_artery\_right | |
| 56 | subclavian\_artery\_left | |
| 57 | common\_carotid\_artery\_right | |
| 58 | common\_carotid\_artery\_left | |
| 59 | brachiocephalic\_vein\_left | |
| 60 | brachiocephalic\_vein\_right | |
| 61 | atrial\_appendage\_left | |
| 62 | superior\_vena\_cava | |
| 63 | inferior\_vena\_cava | |
| 64 | portal\_vein\_and\_splenic\_vein | hepatic portal vein |
| 65 | iliac\_artery\_left | common iliac artery |
| 66 | iliac\_artery\_right | common iliac artery |
| 67 | iliac\_vena\_left | common iliac vein |
| 68 | iliac\_vena\_right | common iliac vein |
| 69 | humerus\_left | |
| 70 | humerus\_right | |
| 71 | scapula\_left | |
| 72 | scapula\_right | |
| 73 | clavicula\_left | clavicle |
| 74 | clavicula\_right | clavicle |
| 75 | femur\_left | |
| 76 | femur\_right | |
| 77 | hip\_left | |
| 78 | hip\_right | |
| 79 | spinal\_cord | |
| 80 | gluteus\_maximus\_left | gluteus maximus muscle |
| 81 | gluteus\_maximus\_right | gluteus maximus muscle |
| 82 | gluteus\_medius\_left | gluteus medius muscle |
| 83 | gluteus\_medius\_right | gluteus medius muscle |
| 84 | gluteus\_minimus\_left | gluteus minimus muscle |
| 85 | gluteus\_minimus\_right | gluteus minimus muscle |
| 86 | autochthon\_left | |
| 87 | autochthon\_right | |
| 88 | iliopsoas\_left | iliopsoas muscle |
| 89 | iliopsoas\_right | iliopsoas muscle |
| 90 | brain | |
| 91 | skull | |
| 92 | rib\_left\_1 | |
| 93 | rib\_left\_2 | |
| 94 | rib\_left\_3 | |
| 95 | rib\_left\_4 | |
| 96 | rib\_left\_5 | |
| 97 | rib\_left\_6 | |
| 98 | rib\_left\_7 | |
| 99 | rib\_left\_8 | |
| 100 | rib\_left\_9 | |
| 101 | rib\_left\_10 | |
| 102 | rib\_left\_11 | |
| 103 | rib\_left\_12 | |
| 104 | rib\_right\_1 | |
| 105 | rib\_right\_2 | |
| 106 | rib\_right\_3 | |
| 107 | rib\_right\_4 | |
| 108 | rib\_right\_5 | |
| 109 | rib\_right\_6 | |
| 110 | rib\_right\_7 | |
| 111 | rib\_right\_8 | |
| 112 | rib\_right\_9 | |
| 113 | rib\_right\_10 | |
| 114 | rib\_right\_11 | |
| 115 | rib\_right\_12 | |
| 116 | sternum | |
| 117 | costal\_cartilages | |
**Class map for task `total_mr`:**
| Index | TotalSegmentator name | TA2 name |
| ----: | -------------------------------- | ---------------------- |
| 1 | spleen | |
| 2 | kidney\_right | |
| 3 | kidney\_left | |
| 4 | gallbladder | |
| 5 | liver | |
| 6 | stomach | |
| 7 | pancreas | |
| 8 | adrenal\_gland\_right | suprarenal gland |
| 9 | adrenal\_gland\_left | suprarenal gland |
| 10 | lung\_left | |
| 11 | lung\_right | |
| 12 | esophagus | |
| 13 | small\_bowel | small intestine |
| 14 | duodenum | |
| 15 | colon | |
| 16 | urinary\_bladder | |
| 17 | prostate | |
| 18 | sacrum | |
| 19 | vertebrae | |
| 20 | intervertebral\_discs | |
| 21 | spinal\_cord | |
| 22 | heart | |
| 23 | aorta | |
| 24 | inferior\_vena\_cava | |
| 25 | portal\_vein\_and\_splenic\_vein | hepatic portal vein |
| 26 | iliac\_artery\_left | common iliac artery |
| 27 | iliac\_artery\_right | common iliac artery |
| 28 | iliac\_vena\_left | common iliac vein |
| 29 | iliac\_vena\_right | common iliac vein |
| 30 | humerus\_left | |
| 31 | humerus\_right | |
| 32 | scapula\_left | |
| 33 | scapula\_right | |
| 34 | clavicula\_left | |
| 35 | clavicula\_right | |
| 36 | femur\_left | |
| 37 | femur\_right | |
| 38 | hip\_left | |
| 39 | hip\_right | |
| 40 | gluteus\_maximus\_left | gluteus maximus muscle |
| 41 | gluteus\_maximus\_right | gluteus maximus muscle |
| 42 | gluteus\_medius\_left | gluteus medius muscle |
| 43 | gluteus\_medius\_right | gluteus medius muscle |
| 44 | gluteus\_minimus\_left | gluteus minimus muscle |
| 45 | gluteus\_minimus\_right | gluteus minimus muscle |
| 46 | autochthon\_left | |
| 47 | autochthon\_right | |
| 48 | iliopsoas\_left | iliopsoas muscle |
| 49 | iliopsoas\_right | iliopsoas muscle |
| 50 | brain | |