TotalSegmentator

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.

Installation

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

Install dependencies:

• Python >= 3.9
• Pytorch >= 2.0.0

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

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.

Subtasks

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 jakob.wasserthal@usb.ch):

• 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, torso_fat, skeletal_muscle
• 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>

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

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. 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 (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.)

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

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:

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:

from totalsegmentator.nifti_ext_header import load_multilabel_nifti

segmentation_nifti_img, label_map_dict = load_multilabel_nifti(image_path)

The above code requires pip install xmltodict.

Install latest master branch (contains latest bug fixes)

pip install git+https://github.com/wasserth/TotalSegmentator.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?)

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.

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.

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_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