Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
Project description
MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
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
- Install PyTorch based on your system requirements
- Install MRSegmentator with pip
Example workflow:
# Create virtual environment
conda create -n mrseg python=3.11 pip
conda activate mrseg
# Install PyTorch (will be different on your system, please refer to the PyTorch documentation)
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 --input <nifti file or directory>
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. 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")]
folds = [0]
inference.infer(images, outdir, 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 |
Project details
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