TotalSegmentator

Tool for segmentation of 104 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. The training dataset with 1204 subjects can be downloaded from Zenodo. You can also try the tool online at totalsegmentator.com.

Created by the department of Research and Analysis at University Hospital Basel.
If you use it please cite our paper: https://arxiv.org/abs/2208.05868.

Installation

Install dependencies:

Python >= 3.7
Pytorch
if you use the option --preview you have to install xvfb (apt-get install xvfb)
You should not have any nnU-Net installation in your python environment since TotalSegmentator will install its own custom installation.
TotalSegmentator was developed for Linux or Mac. To make it work for windows see this comment.

Install Totalsegmentator

pip install TotalSegmentator

Usage

TotalSegmentator -i ct.nii.gz -o segmentations --fast --preview

Note: TotalSegmentator only works with a NVidia GPU. If you do not have one you can try our online tool: www.totalsegmentator.com

Advanced settings

--fast: For faster runtime and less memory requirements use this option. It will run a lower resolution model (3mm instead of 1.5mm).
--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).
--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 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:/workspace wasserth/totalsegmentator_container:master TotalSegmentator -i /workspace/ct.nii.gz -o /workspace/segmentations

Resource Requirements

Totalsegmentator has the following runtime and memory requirements:
(1.5mm is the normal model and 3mm is the --fast model)

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 shown these numbers in the supplementary materials figure 11.

Retrain model on your own

You have to download the data and then follow the instructions of nnU-Net how to train a nnU-Net. We trained a 3d_fullres model and the only adaptation to the default training is setting the number of epochs to 4000 and deactivating mirror data augmentation. The adapted trainer can be found here.

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

Install latest master branch (contains latest bug fixes)

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

Reference

For more details see this paper https://arxiv.org/abs/2208.05868. If you use this tool please cite it as follows

Wasserthal J., Meyer M., Breit H., Cyriac J., Yang S., Segeroth M. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images, 2022. URL: https://arxiv.org/abs/2208.05868.  arXiv: 2208.05868

Moreover, we would really appreciate 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.

DLwbm123 / TotalSegmentator