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Tool for segmentation of over 117 classes in CT images. It was trained on a wide range of different CT images (different scanners, institutions, protocols,...) and therefore should work well on most images. A large part of the training dataset can be downloaded from Zenodo (1228 subjects). You can also try the tool online at

ANNOUNCEMENT: We recently released v2. See changes and improvements.

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Created by the department of Research and Analysis at University Hospital Basel.
If you use it please cite our Radiology AI paper. Please also cite nnUNet since TotalSegmentator is heavily based on it.


TotalSegmentator works on Ubuntu, Mac, and Windows and on CPU and GPU.

Install dependencies:


  • if you use the option --preview you have to install xvfb (apt-get install xvfb)

Install Totalsegmentator

pip install TotalSegmentator


TotalSegmentator -i ct.nii.gz -o segmentations

Note: A Nifti file or a folder with all DICOM slices of one patient is allowed as input

Note: If you run on CPU use the option --fast or --roi_subset to greatly improve runtime.

Note: This is not a medical device and is not intended for clinical usage.


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Next to the default task (total) there are more subtasks with more classes:

Openly available for any usage:

  • total: default task containing 117 main classes (see here for a list of classes)
  • lung_vessels: lung_vessels (cite paper), lung_trachea_bronchia
  • body: body, body_trunc, body_extremities, skin
  • cerebral_bleed: intracerebral_hemorrhage (cite paper)*
  • hip_implant: hip_implant*
  • coronary_arteries: coronary_arteries*
  • pleural_pericard_effusion: pleural_effusion (cite paper), pericardial_effusion (cite paper)*

*: 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. For a commercial license contact

  • heartchambers_highres: myocardium, atrium_left, ventricle_left, atrium_right, ventricle_right, aorta, pulmonary_artery (trained on sub-millimeter resolution)
  • appendicular_bones: patella, tibia, fibula, tarsal, metatarsal, phalanges_feet, ulna, radius, carpal, metacarpal, phalanges_hand
  • tissue_types: subcutaneous_fat, skeletal_muscle, torso_fat
  • vertebrae_body: vertebral body of all vertebrae (without the vertebral arch)
  • face: face_region


TotalSegmentator -i ct.nii.gz -o segmentations -ta <task_name>

Advanced settings

  • --device: Choose cpu or gpu
  • --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.
  • --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).
  • --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 for index to class name mapping).
  • --statistics: This will generate a file statistics.json with volume (in mm³) and mean intensity of each class.
  • --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).

Run via docker

We also provide a docker container which can be used the following way

docker run --gpus 'device=0' --ipc=host -v /absolute/path/to/my/data/directory:/tmp wasserth/totalsegmentator:2.0.0 TotalSegmentator -i /tmp/ct.nii.gz -o /tmp/segmentations

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:

pip install TotalSegmentator==1.5.7

The documentation for v1 can be found here. Bugfixes for v1 are developed in the branch v1_bugfixes. Our Radiology AI publication refers to TotalSegmentator v1.

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.)

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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
  • --nr_thr_saving 1: Saving big images with several threads will take a lot of memory

Train/validation/test split

The exact split of the dataset can be found in the file meta.csv inside of the dataset. 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. The paper shows these numbers in the supplementary materials Figure 11.

Retrain model and run evaluation

See here 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.

Other commands

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:

totalseg_combine_masks -i totalsegmentator_output_dir -o combined_mask.nii.gz -m lung

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:

totalseg_download_weights -t <task_name>

After acquiring a license number for the non-open tasks you can set it with the following command:

totalseg_set_license -l aca_12345678910

Python API

You can run totalsegmentator via Python:

from totalsegmentator.python_api import totalsegmentator

if __name__ == "__main__":
    totalsegmentator(input_path, output_path)

You can see all available arguments here. Running from within the main environment should avoid some multiprocessing issues.

Install latest master branch (contains latest bug fixes)

pip install git+

Typical problems

ITK loading Error
When you get the following error message

ITK ERROR: ITK only supports orthonormal direction cosines. No orthonormal definition was found!

you should do

pip install SimpleITK==2.0.2

Alternatively you can try

fslorient -copysform2qform input_file
fslreorient2std input_file output_file

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?)


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.


For more details see our Radiology AI paper (freely available preprint). If you use this tool please cite it as follows

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.

Please also cite 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.

Class details

The following table shows a list of all classes.

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 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_right_4
93 rib_right_3
94 rib_left_1
95 rib_left_2
96 rib_left_3
97 rib_left_4
98 rib_left_5
99 rib_left_6
100 rib_left_7
101 rib_left_8
102 rib_left_9
103 rib_left_10
104 rib_left_11
105 rib_left_12
106 rib_right_1
107 rib_right_2
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