TotalSegmentator

June 1, 2026 · View on GitHub

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 (1228 subjects) and MR dataset (616 subjects). You can also try the tool online at totalsegmentator.com or as 3D Slicer extension.

ANNOUNCEMENT: We created a platform where anyone can help annotate more data to further improve TotalSegmentator: TotalSegmentator Annotation Platform.

ANNOUNCEMENT: We created web applications for abdominal organ volume, aorta diameter, pulmonary artery diameter, contrast phase detection and Evans index.

Main classes for CT and MR: Alt text

TotalSegmentator supports a lot more structures. See subtasks or here for more details.

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

Installation

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

Install dependencies:

Optionally:

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

Install Totalsegmentator

pip install TotalSegmentator

Usage

For CT images:

TotalSegmentator -i ct.nii.gz -o segmentations

For MR images:

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

Alt text

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 for a list of classes; see here for more details)
  • total_mr: default task containing 50 main classes on MR images (see here for a list of classes)
  • lung_vessels: lung_arteries, lung_veins, lung_airways, lung_airways_wall (partly based on paper, also cite paper)
  • lung_vessels_LEGACY: lung_vessels, lung_trachea_bronchia (cite paper)
  • 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)*
  • hip_implant: hip_implant* (download training dataset)
  • pleural_pericard_effusion: pleural_effusion (cite paper), pericardial_effusion (cite paper, download training dataset)*
  • 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)
  • 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)
  • 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)
  • liver_vessels: liver_vessels, liver_tumor (cite paper)*
  • 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: 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)*
  • 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)*
  • craniofacial_structures: mandible, teeth_lower, skull, head, sinus_maxillary, sinus_frontal, teeth_upper (cite paper)
  • 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) (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)
  • 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, download training dataset)*
  • liver_lesions_mr: liver_lesions (for MR images) (cite paper, download training dataset)*

*: 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 jakob.wasserthal@usb.ch):

  • heartchambers_highres: myocardium, atrium_left, ventricle_left, atrium_right, ventricle_right, aorta, pulmonary_artery (trained on sub-millimeter resolution; details)
  • 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)
  • 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 as our model is partly based on this)
  • vertebrae_body: vertebral body of all vertebrae (without the vertebral arch), intervertebral_discs (for MR this is part of the total_mr task)
  • 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) (CC BY-NC 4.0 license, no commercial license available) (cite paper)

Usage:

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

Confused by all the structures and tasks? Check this to search through available structures and tasks.

The mapping from label ID to class name can be found here.

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 for providing a team of radiologists to support some of the data annotations.

Advanced settings

  • --device: Choose cpu or gpu or gpu:X (e.g., gpu:1 -> cuda:1)
  • --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).
  • --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).
  • --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).
  • --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).

Other commands

If you want to know body weight, size, age, sex, BMI and BSA 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. More details can be found here:

totalseg_get_body_stats -i ct.nii.gz -o body_stats.json -m ct

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:

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).

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:

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

If you want to calculate the Evans index you can use the following command:

totalseg_evans_index -i ct_skull.nii.gz -o evans_index.json -p evans_index.png

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>

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:

totalseg_set_license -l aca_12345678910

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.

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.

Web applications

We provide the following web applications to easily process your images:

Run via docker

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

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.)

Alt text

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:

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. 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):

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)

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. 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.

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

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]

or use this python command.

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:

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.

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 you can see an overview of differences between v1 and v2.

Reference

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.

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 you can find a mapping of the TotalSegmentator classes to SNOMED-CT codes.

IndexTotalSegmentator nameTA2 name
1spleen
2kidney_right
3kidney_left
4gallbladder
5liver
6stomach
7pancreas
8adrenal_gland_rightsuprarenal gland
9adrenal_gland_leftsuprarenal gland
10lung_upper_lobe_leftsuperior lobe of left lung
11lung_lower_lobe_leftinferior lobe of left lung
12lung_upper_lobe_rightsuperior lobe of right lung
13lung_middle_lobe_rightmiddle lobe of right lung
14lung_lower_lobe_rightinferior lobe of right lung
15esophagus
16trachea
17thyroid_gland
18small_bowelsmall intestine
19duodenum
20colon
21urinary_bladder
22prostate
23kidney_cyst_left
24kidney_cyst_right
25sacrum
26vertebrae_S1
27vertebrae_L5
28vertebrae_L4
29vertebrae_L3
30vertebrae_L2
31vertebrae_L1
32vertebrae_T12
33vertebrae_T11
34vertebrae_T10
35vertebrae_T9
36vertebrae_T8
37vertebrae_T7
38vertebrae_T6
39vertebrae_T5
40vertebrae_T4
41vertebrae_T3
42vertebrae_T2
43vertebrae_T1
44vertebrae_C7
45vertebrae_C6
46vertebrae_C5
47vertebrae_C4
48vertebrae_C3
49vertebrae_C2
50vertebrae_C1
51heart
52aorta
53pulmonary_vein
54brachiocephalic_trunk
55subclavian_artery_right
56subclavian_artery_left
57common_carotid_artery_right
58common_carotid_artery_left
59brachiocephalic_vein_left
60brachiocephalic_vein_right
61atrial_appendage_left
62superior_vena_cava
63inferior_vena_cava
64portal_vein_and_splenic_veinhepatic portal vein
65iliac_artery_leftcommon iliac artery
66iliac_artery_rightcommon iliac artery
67iliac_vena_leftcommon iliac vein
68iliac_vena_rightcommon iliac vein
69humerus_left
70humerus_right
71scapula_left
72scapula_right
73clavicula_leftclavicle
74clavicula_rightclavicle
75femur_left
76femur_right
77hip_left
78hip_right
79spinal_cord
80gluteus_maximus_leftgluteus maximus muscle
81gluteus_maximus_rightgluteus maximus muscle
82gluteus_medius_leftgluteus medius muscle
83gluteus_medius_rightgluteus medius muscle
84gluteus_minimus_leftgluteus minimus muscle
85gluteus_minimus_rightgluteus minimus muscle
86autochthon_left
87autochthon_right
88iliopsoas_leftiliopsoas muscle
89iliopsoas_rightiliopsoas muscle
90brain
91skull
92rib_left_1
93rib_left_2
94rib_left_3
95rib_left_4
96rib_left_5
97rib_left_6
98rib_left_7
99rib_left_8
100rib_left_9
101rib_left_10
102rib_left_11
103rib_left_12
104rib_right_1
105rib_right_2
106rib_right_3
107rib_right_4
108rib_right_5
109rib_right_6
110rib_right_7
111rib_right_8
112rib_right_9
113rib_right_10
114rib_right_11
115rib_right_12
116sternum
117costal_cartilages

Class map for task total_mr:

IndexTotalSegmentator nameTA2 name
1spleen
2kidney_right
3kidney_left
4gallbladder
5liver
6stomach
7pancreas
8adrenal_gland_rightsuprarenal gland
9adrenal_gland_leftsuprarenal gland
10lung_left
11lung_right
12esophagus
13small_bowelsmall intestine
14duodenum
15colon
16urinary_bladder
17prostate
18sacrum
19vertebrae
20intervertebral_discs
21spinal_cord
22heart
23aorta
24inferior_vena_cava
25portal_vein_and_splenic_veinhepatic portal vein
26iliac_artery_leftcommon iliac artery
27iliac_artery_rightcommon iliac artery
28iliac_vena_leftcommon iliac vein
29iliac_vena_rightcommon iliac vein
30humerus_left
31humerus_right
32scapula_left
33scapula_right
34clavicula_left
35clavicula_right
36femur_left
37femur_right
38hip_left
39hip_right
40gluteus_maximus_leftgluteus maximus muscle
41gluteus_maximus_rightgluteus maximus muscle
42gluteus_medius_leftgluteus medius muscle
43gluteus_medius_rightgluteus medius muscle
44gluteus_minimus_leftgluteus minimus muscle
45gluteus_minimus_rightgluteus minimus muscle
46autochthon_left
47autochthon_right
48iliopsoas_leftiliopsoas muscle
49iliopsoas_rightiliopsoas muscle
50brain