MRISegmenter

MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI

Yan Zhuang¹, Tejas Sudharshan Mathai², Pritam Mukherjee², Brandon Khoury³, Boah Kim⁴, Benjamin Hou², Nusrat Rabbee⁵, Abhinav Suri², and Ronald M. Summers²

¹ Department of Diagnostic, Molecular, and Interventional Radiology, Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
² Department of Radiology and Imaging Sciences, Imaging Biomarkers and Computer Aided Diagnosis Laboratory, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Rm 1C224D, Bethesda, MD, USA
³ Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
⁴ Department of MetaBioHealth, Sungkyunkwan University, Seoul, South Korea
⁵ Department of Biostatistics and Clinical Epidemiology Services, National Institutes of Health Clinical Center, Bethesda, MD, USA

[Paper]

License

Please check out the license file.

Acknowledgement: This work was supported by the Intramural Research Program of the National Institutes of Health (NIH) Clinical Center (project number 1Z01 CL040004). This work used the computational resources of the NIH HPC Biowulf cluster. Y.Z is supported in part by the Eric and Wendy Schmidt AI in Human Health Fellowship Program at Icahn School of Medicine at Mount Sinai. This work utilized the computational resources of the NIH HPC Biowulf cluster.

Usage

Requirements: We recommend running on a computer with a GPU. This package can be run on a computer with a CPU, but it will take a very long time to process a single scan.

Step 1: Create a virtual environment and install the package.
We recommend you install MRISegmentator in a conda environment to avoid dependency conflicts. Note you can use any version of python that supports nnUNet v2.2 or above

conda create -n MRISegmentator python=3.11
conda activate MRISegmentator  
pip install MRISegmentator

Step 2: Run!

MRISegmentator -i path/to/input/mri.nii.gz -o path/to/output/segmentation.nii.gz -d gpu

Notes:

• The model weights will download on their own to one of the following directories:

  • if the environment variable MRISEGMENTATOR_DIR is set, we will download to that directory (and create the directory if it does not exist)
  • if that environment variable is not set, it will download to the home directory at ~/.mrisegmentator_weights.
  • You can also specify a directory for the weights via the -m option (this must be a path to the extracted folder from this zip file)

• For the -d option, you can also provide cpu or mps as an option (cpu runs on your computer's CPU only and mps runs on M1/2 processors).

Python API

You can also run this package via importing it in a python script:

from mrisegmentator.inference import mri_segmentator

if __name__ == '__main__':
    input_file_path = # path to your input file /mypath/input/input.nii.gz
    output_file_path = # path to where you want to segmentation to save. e.g. /mypath/result/out.nii.gz
    device = # one of 'gpu', 'cpu', 'mps'
    path_to_model = 'None' # it will automatically download the model weights, so just configure it as None
    mri_segmentator(input_file_path, output_file_path, path_to_model, device)

Redownloading weights

Normally, we handle downloading the weights for you, but if we release a new model version, we will need you to redownload the weights via the following command

MRISegmentator_Redownload

The last time model weights were changed was on May 30, 2024.

Issues

MRISegmentator is a research-grade segmentation tool currently under active development. Please let us know if you encounter any issues or have suggestions for improvements.

References

If you find our work is useful for your research, please cite

@article{zhuang2025,
title={MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI},
  author={Yan Zhuang, Tejas Sudharshan Mathai, Pritam Mukherjee, Brandon Khoury, Boah Kim, Benjamin Hou, Nusrat Rabbee, Abhinav Suri, and Ronald M. Summers},
  journal={Radiology},
  year={2025}
}

@article{zhuang2024mrisegmentator,
  title={MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI},
  author={Zhuang, Yan and Mathai, Tejas Sudharshan and Mukherjee, Pritam and Khoury, Brandon and Kim, Boah and Hou, Benjamin and Rabbee, Nusrat and Suri, Abhinav and Summers, Ronald M},
  journal={arXiv preprint arXiv:2405.05944},
  year={2024}
}

We used nnUnet in our research, please also consider citing

@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

Segmentation labels

Below is a table that maps the segmentation codes to the original bodypart name, or

Here you can find the itk-snap label description.

Organ or Structure name	Label
spleen	1
kidney_right	2
kidney_left	3
gallbladder	4
liver	5
esophagus	6
stomach	7
aorta	8
inferior_vena_cava	9
portal_vein_and_splenic_vein	10
pancreas	11
adrenal_gland_right	12
adrenal_gland_left	13
lung_right	14
lung_left	15
small_bowel	16
duodenum	17
colon	18
iliac_artery_left	19
iliac_artery_right	20
iliac_vena_left	21
iliac_vena_right	22
gluteus_maximus_left	23
gluteus_maximus_right	24
gluteus_medius_left	25
gluteus_medius_right	26
autochthon_left	27
autochthon_right	28
iliopsoas_left	29
iliopsoas_right	30
hip_left	31
hip_right	32
sacrum	33
rib_left_4	34
rib_left_5	35
rib_left_6	36
rib_left_7	37
rib_left_8	38
rib_left_9	39
rib_left_10	40
rib_left_11	41
rib_left_12	42
rib_right_4	43
rib_right_5	44
rib_right_6	45
rib_right_7	46
rib_right_8	47
rib_right_9	48
rib_right_10	49
rib_right_11	50
rib_right_12	51
vertebrae_L5	52
vertebrae_L4	53
vertebrae_L3	54
vertebrae_L2	55
vertebrae_L1	56
vertebrae_T12	57
vertebrae_T11	58
vertebrae_T10	59
vertebrae_T9	60
vertebrae_T8	61
vertebrae_T7	62