Detect and segment 40 classes in MRI scans of the abdominal / pelvic / thorax region
Contrary to CT scans, where tools for automatic multi-structure segmentation are quite mature, segmentation tasks in MRI scans are often either focused on the brain region or on a subset of few organs in other body regions. MRSegmentator aims to extend this and accurately segment 40 organs and structures in human MRI scans of the abdominal, pelvic and thorax regions. The segmentation works well on different sequence types, including T1- and T2-weighted, Dixon sequences and even CT images. Read more about it in our preprint: https://arxiv.org/pdf/2405.06463.
Check out some sample segmentations on our Hugging Face Space! 🤗
Understand the model in depth by reading our Evaluation section.
Install MRSegmentator with pip:
# Create virtual environment
conda create -n mrseg python=3.11 pip
conda activate mrseg
# Install MRSegmentator
python -m pip install mrsegmentator(Optionally) If the installed pytorch version coming with nnunet is not compatible to your system, you might need to install it manually, please refer to PyTorch.
MRSegmentator segments all .nii and .nii.gz files in an input directory and writes segmentations to the specified output directory. To speed up segmentation you can increase the --batchsize or select a single model for inference with --fold 0.
MRSegmentator requires a lot of memory and can run into OutOfMemory exceptions when used on very large images. You can reduce memory usage by setting --split_level to 1 or 2. Be aware that this increases runtime. Read more about the options in the Evaluation section.
mrsegmentator --input <nifti file or directory>Options:
-i, --input <str> [required] # input directory or file
--outdir <str> # output directory
--fold <int> # use only a single model for inference
--postfix <str> # postfix that will be added to segmentations, default: "seg"
--cpu_only # don't use a gpu
# memory (mutually exclusive)
--batchsize <int> # number of images that can be loaded to memory at the same time, default: 8
--split_level <int> # split images to reduce memory usage. Images are split recursively: A split level of x will produce 2^x smaller images
# experimental
--nproc <int> # number of processes
--nproc_export <int> # number of processes for exporting the segmentations
--verbosefrom mrsegmentator import inference
import os
outdir = "outputdir"
images = [f.path for f in os.scandir("image_dir")]
inference.infer(images, outdir)If you use our work in your research, please cite our preprint on arXiv: https://arxiv.org/pdf/2405.06463.
| Index | Class |
|---|---|
| 0 | background |
| 1 | spleen |
| 2 | right_kidney |
| 3 | left_kidney |
| 4 | gallbladder |
| 5 | liver |
| 6 | stomach |
| 7 | pancreas |
| 8 | right_adrenal_gland |
| 9 | left_adrenal_gland |
| 10 | left_lung |
| 11 | right_lung |
| 12 | heart |
| 13 | aorta |
| 14 | inferior_vena_cava |
| 15 | portal_vein_and_splenic_vein |
| 16 | left_iliac_artery |
| 17 | right_iliac_artery |
| 18 | left_iliac_vena |
| 19 | right_iliac_vena |
| 20 | esophagus |
| 21 | small_bowel |
| 22 | duodenum |
| 23 | colon |
| 24 | urinary_bladder |
| 25 | spine |
| 26 | sacrum |
| 27 | left_hip |
| 28 | right_hip |
| 29 | left_femur |
| 30 | right_femur |
| 31 | left_autochthonous_muscle |
| 32 | right_autochthonous_muscle |
| 33 | left_iliopsoas_muscle |
| 34 | right_iliopsoas_muscle |
| 35 | left_gluteus_maximus |
| 36 | right_gluteus_maximus |
| 37 | left_gluteus_medius |
| 38 | right_gluteus_medius |
| 39 | left_gluteus_minimus |
| 40 | right_gluteus_minimus |
This work was in large parts funded by the Wilhelm Sander Foundation. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them.
