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 totalsegmentator.com.
ANNOUNCEMENT: We recently released v2. See changes and improvements.
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:
- Python >= 3.9
- Pytorch >= 1.12.1
Optionally:
- if you use the option
--previewyou 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
--fastor--roi_subsetto greatly improve runtime.
Note: This is not a medical device and is not intended for clinical usage.
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 [email protected]):
- 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
Usage:
TotalSegmentator -i ct.nii.gz -o segmentations -ta <task_name>
--device: Choosecpuorgpu--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).--preview: This will generate a 3D rendering of all classes, giving you a quick overview if the segmentation worked and where it failed (seepreview.pngin 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 filestatistics.jsonwith volume (in mm³) and mean intensity of each class.--radiomics: This will generate a filestatistics_radiomics.jsonwith the radiomics features of each class. You have to install pyradiomics to use this (pip install pyradiomics).
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
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.
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--nr_thr_saving 1: Saving big images with several threads will take a lot of memory
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.
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.
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
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.
pip install git+https://github.com/wasserth/TotalSegmentator.git
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. https://doi.org/10.1148/ryai.230024
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.
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 |