mrsegmentator 1.2.0

Multi-Modality Segmentation of 40 Classes in MRI and CT

MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences

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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! 🤗

Update v1.2:

We moved the NAKO dataset from the test to the training-pipeline and retrained the model (See Updated Weights). You can use the previous version, without NAKO images, by setting the version to 1.1 during installation with pip.

You can update to the new version with:

python -m pip install --upgrade mrsegmentator==1.2

(Make sure to include the version number, sometimes pip doesn't do what you'd expect it to do.)

Installation

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.

Inference

MRSegmentator segments all .nii and .nii.gz files in an input directory and writes segmentations to the specified output directory. MRSegmentator requires a lot of memory and can run into OutOfMemory exceptions when used on very large images (e.g. some CT scans). You can reduce memory usage by setting --split_level to 1 or 2. Be aware that this increases runtime and possibly reduces segmentation performance.

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"
--split_level <int> # split images to reduce memory usage. Images are split recusively: A split level of x will produce 2^x smaller images.

--batchsize <int> # how many images can be loaded to memory at the same time, default: 8
--nproc <int> # number of processes
--nproc_export <int> # number of processes for exporting the segmentations
--cpu_only # don't use a gpu
--verbose

Python API

from mrsegmentator import inference
import os

outdir = "outputdir"
images = [f.path for f in os.scandir("image_dir")]

inference.infer(images, outdir)

How To Cite

If you use our work in your research, please cite our preprint on arXiv: https://arxiv.org/pdf/2405.06463.

Hartmut Häntze, Lina Xu, Felix J. Dorfner, Leonhard Donle, Daniel Truhn, Hugo Aerts, Mathias Prokop, Bram
van Ginneken, Alessa Hering, Lisa C. Adams, and Keno K. Bressem. MRSegmentator: Robust multi-modality
segmentation of 40 classes in MRI and CT sequences. arXiv, 2024.

Class details

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

Acknowledgements

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.

Changelog

v1.2.0 (22/08/2024)

Feature

• Add NAKO data to training pipeline
• Update weights

Fix

• Make ensemble prediction default for Python API

v1.1.2 (24/06/2024)

Fix

• Change python_requires from 3.11 to 3.9
• Remove monai dependency

v1.1.0 (18/05/2024)

Feature

• Update model weights with weights trained by nnUNetTrainerNoMirroring

Fix

• Remove postprocessing remap_left_right(...). It is not needed anymore.

v1.0.0 (10/05/2024)

• First release of MRSegmentator