mrsegmentator 1.0.0

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

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.

Installation

1. Install PyTorch based on your system requirements
2. Install MRSegmentator with pip
3. Download the weights and extract them into your model directory

Example workflow:

# Create virtual environment
conda create -n mrseg python=3.11 pip
conda activate mrseg

# Install pytorch
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

# Install MRSegmentator
python -m pip install mrsegmentator

# Inference
mrsegmentator  \
--modeldir "/sc-projects/sc-proj-cc06-ag-ki-radiologie/Niere/ukbb/ckpts/mr_segmentator_weights/" \
--input <nifti file or directory> \
--outdir <directory> 

# Download Weights (TODO) (Currently the weights are stored on the cluster)
# wget https://www.url-placeholder.de/weights.zip
# unzip weights.zip

Inference

MRSegmentator segments all .nii and .nii.gz files in an input directory and writes segmentations to the specified output directory. MRSegmentator was trained on images in LPS orientation and automatically transforms input images accordingly. Afterwards, the segmenation's orientation will be changed back to match the original image. If you are certain that your images are in the LPS orientation you can skip this preoprocessing step by setting the --is_LPS flag (this significantly reduces runtime). 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 --modeldir <model directory> \
    --input <input directory or file> \
    --outdir <output directory> 

Options:

--modeldir <str> [required]  # model directory
--input <str> [required] # input directory or file
--outdir <str> [required] # output directory

--fold <int> # use only a single model for inference 
--crossval # Run all 5 models individually. Useful to analyse differences between the models.

--is_LPS # if your images are in LPS orientation you can set this flag to skip one preprocessing step. This decreases runtime
--postfix <str> # postfix that will be added to segmentations. Default: "seg"
--cpu_only # don't use a gpu
--verbose
--batchsize <int> # how many images can be loaded to memory at the same time, ideally this should equal the dataset size
--nproc <int> # number of processes
--nproc_export <int> # number of processes for exporting the segmentations
--split_level <int> # split images to reduce memory usage. Images are split recusively: A split level of x will produce 2^x smaller images.

Python API

from mrsegmentator import inference
import os

modeldir = "mrseg_weights"
outdir = "outputdir"
images = [f.path for f in os.scandir("image_dir")]
folds = [0]

inference.infer(modeldir, outdir, images, folds)

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