You can select older versions with --dataset_id [ID]
| ID | NAME | Version | Modality | Resolution | Link |
|---|---|---|---|---|---|
| 100 | VIBESegmentator | current | MRI/CT | axial 1.41 mm, S=3mm | preprint |
| 99 | VIBESegmentator | current (sagittal resolution) | MRI/CT | sagittal 1.41 mm, R=3mm | preprint |
| 278 | Splits the body in 11 regions | current | NAKO VIBE-only | iso 4 mm | |
| 282 | Torso Water-Fat-swap detection | current (use script from linked repo) | multi-echo-Vibe VIBE | axial 1.41 mm, S=3mm | preprint; Other Repo |
| 511 | Spine Vertebra instance | Fast, but may produce off-by-one errors. Use SPINEPS if this method fails. | VIBE-only | axial 1.41 mm, S=3mm | See also |
| 512 | Spine Vertebra subregion | incorporated in SPINEPS | VIBE-only | axial 1.41 mm, S=3mm | See also |
| 520 | CT Bone segmentations | preliminary | CT | iso 0.8 mm |
| ID | NAME | Version | Modality | Resolution | Link |
|---|---|---|---|---|---|
| 98 | VIBESegmentator | (deprecated-spinal channel bugged in CT) | MRI | axial 1.41 mm, S=3mm | |
| 80 | VIBESegmentator | preprint (deprecated) | MRI | axial 1.41 mm, S=3mm | |
| 85-87 | VIBESegmentator | early preprint (deprecated) | MRI | axial 1.41 mm, S=3mm |
- Python 3.10 or higher.
- Tested on Ubuntu and Windows.
- One of the following:
- Nvidia-GPU with 4 GB of RAM or more.
- A mps device (like a newer Mac with M2/M3 , but we could not test this.) (--ddevice mps)
- A strong CPU (--ddevice cpu)
- Open a command line (search for Terminal on Mac or cmd on Windows in your OS search).
- Ensure you have Anaconda (Python) installed.
- Navigate to the folder where you want to download the script using
cd [FOLDER PATH].
# Run this commands by coping in to the Terminal
# Recommended: make a virtual Python environment (example shows Anaconda)
conda create -n "VIBESegmentator" python=3.11.0
conda activate VIBESegmentator
# Install PyTorch that works with your GPU (follow instructions at https://pytorch.org/get-started/locally/)
pip install torch torchvision torchaudio
# Install required Python packages
pip install TPTBox ruamel.yaml configargparse
pip install nnunetv2
# We recommend the newest versions. Tested versions: TPTBox==1.6, ruamel.yaml==0.18.6, configargparse==1.7, nnunetv2==2.4.2
# If e. g. nnunetv2 does not work, try version 2.4.2
# Uninstall the current version and reinstall with the specified version
#pip uninstall nnunetv2
#pip install nnunetv2==2.4.2
# Download the scripts (they will be downloaded to your current folder)
git clone https://github.com/robert-graf/VIBESegmentator.git
cd VIBESegmentatorIt should take less than 30 minutes to install, excluding installing conda/python. The longest time takes to install pytroch.
Download the nnUNet weights automatically. They are put in [Path to this project]/nnUNet/nnUNet_results/.
# Run this commands in a Terminal where you navigate to the folder where `run_VIBESegmentator.py` is
conda activate VIBESegmentator
# Total Segmentation
python run_VIBESegmentator.py --img [IMAGE-PATH] --out_path [OUTPATH] --ddevice [cpu|cuda|mpu (optional)]
# Total Segmentation with postprocessing and combining of masks
python run_VIBESegmentator_multi.py --img_inphase [IMAGE-PATH] --img_water [IMAGE-PATH] --img_outphase [IMAGE-PATH] --out_path [OUTPATH] --ddevice [cpu|cuda|mpu (optional)]
# Spine Instance
python run_instance_spine_segmentation.py --img [IMAGE-PATH] --out_path [OUTPATH] --ddevice [cpu|cuda|mpu (optional)]
# Spine Semantic
python run_semantic_spine_segmentation.py --img [IMAGE-PATH] --out_path [OUTPATH] --ddevice [cpu|cuda|mpu (optional)]
conda activate VIBESegmentator
#cd to the folder where you downloaded the GitHub files (the folder where the run_VIBESegmentator.py is located)Let's run some examples that were never seen during training. You can download all four examples from here
(Source: link Number s0284, s0286, s0287, s0288)
Unzip the file. For this example, we copied all files to a folder called 'example' where the run_VIBESegmentator.py is located.
python run_VIBESegmentator.py --img example/mri1.nii.gz --out_path example/seg1.nii.gz
python run_VIBESegmentator.py --img example/mri2.nii.gz --out_path example/seg2.nii.gz
python run_VIBESegmentator.py --img example/mri3.nii.gz --out_path example/seg3.nii.gz
python run_VIBESegmentator.py --img example/mri4.nii.gz --out_path example/seg4.nii.gzThe segmentation is stored in "example/segX.nii.gz." You can view the results with ITKSnap by first drag-and-drop the mriX.nii.gz and then drag-and-drop the segX.nii.gz (with X is a number from before) and click on "Open as Segmentation"
To run this script on your data, update the paths.
| ID | NAME |
|---|---|
| 1 | spleen |
| 2 | kidney_right |
| 3 | kidney_left |
| 4 | gallbladder |
| 5 | liver |
| 6 | stomach |
| 7 | pancreas |
| 8 | adrenal_gland_right |
| 9 | adrenal_gland_left |
| 10 | lung_upper_lobe_left |
| 11 | lung_lower_lobe_left |
| 12 | lung_upper_lobe_right |
| 13 | lung_middle_lobe_right |
| 14 | lung_lower_lobe_right |
| 15 | esophagus |
| 16 | trachea |
| 17 | thyroid_gland |
| 18 | intestine |
| 19 | duodenum |
| 20 | unused |
| 21 | urinary_bladder |
| 22 | prostate |
| 23 | sacrum |
| 24 | heart |
| 25 | aorta |
| 26 | pulmonary_vein |
| 27 | brachiocephalic_trunk |
| 28 | subclavian_artery_right |
| 29 | subclavian_artery_left |
| 30 | common_carotid_artery_right |
| 31 | common_carotid_artery_left |
| 32 | brachiocephalic_vein_left |
| 33 | brachiocephalic_vein_right |
| 34 | atrial_appendage_left |
| 35 | superior_vena_cava |
| 36 | inferior_vena_cava |
| 37 | portal_vein_and_splenic_vein |
| 38 | iliac_artery_left |
| 39 | iliac_artery_right |
| 40 | iliac_vena_left |
| 41 | iliac_vena_right |
| 42 | humerus_left |
| 43 | humerus_right |
| 44 | scapula_left |
| 45 | scapula_right |
| 46 | clavicula_left |
| 47 | clavicula_right |
| 48 | femur_left |
| 49 | femur_right |
| 50 | hip_left |
| 51 | hip_right |
| 52 | spinal_cord |
| 53 | gluteus_maximus_left |
| 54 | gluteus_maximus_right |
| 55 | gluteus_medius_left |
| 56 | gluteus_medius_right |
| 57 | gluteus_minimus_left |
| 58 | gluteus_minimus_right |
| 59 | autochthon_left |
| 60 | autochthon_right |
| 61 | iliopsoas_left |
| 62 | iliopsoas_right |
| 63 | sternum |
| 64 | costal_cartilages |
| 65 | subcutaneous_fat |
| 66 | muscle |
| 67 | inner_fat |
| 68 | IVD |
| 69 | vertebra_body |
| 70 | vertebra_posterior_elements |
| 71 | spinal_channel |
| 72 | bone_other |
Open access paper: doi:10.1007/s00330-025-12035-9
The related paper is available as preprint: arXiv:2406.00125
@article{graf2025vibesegmentator,
title={VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank},
author={Graf, Robert and Platzek, Paul and Riedel, Evamaria Olga and Ramsch{\"u}tz, Constanze and Starck, Sophie and M{\"o}ller, Hendrik K and Atad, Matan and V{\"o}lzke, Henry and B{\"u}low, Robin and Schmidt, Carsten Oliver and others},
journal={European Radiology},
pages={1--15},
year={2025},
publisher={Springer}
}
run_VIBESegmentator.py [...] --dataset_id 278
