Metadata-Version: 2.4
Name: mrsegmentator
Version: 2.0.0
Summary: Multi-Modality Segmentation of 40+10 Classes in MRI and CT
Home-page: https://github.com/hhaentze/mrsegmentator
Author: Hartmut Häntze
Author-email: hartmut.haentze@charite.de
Project-URL: Bug Tracker, https://github.com/hhaentze/mrsegmentator/issues
Project-URL: repository, https://github.com/hhaentze/mrsegmentator
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: <3.14,>=3.10
Description-Content-Type: text/markdown
Requires-Dist: torch!=2.9,<=2.10,>=2.3
Requires-Dist: numpy>=1.26
Requires-Dist: nnunetv2<=2.8.0,>=2.2.1
Requires-Dist: pydicom
Requires-Dist: highdicom
Provides-Extra: dev
Requires-Dist: ruff; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pytest-html; extra == "dev"

<h2 align="center"> MRSegmentator: Multi-Modality Segmentation of 40+10 Classes in MRI and CT </h2> 

***

<div align="center">
<a href="https://github.com/hhaentze/MRSegmentator/actions"><img alt="Continuous Integration" src="https://github.com/hhaentze/MRSegmentator/actions/workflows/ci.yml/badge.svg"></a>
<a href="https://github.com/hhaentze/MRSegmentator/blob/master/License.txt"><img alt="License: Apache" src="https://img.shields.io/badge/License-Apache_2.0-blue.svg"></a>  
<a href="https://pypi.org/project/mrsegmentator/"><img alt="PyPI" src="https://img.shields.io/pypi/v/mrsegmentator"></a>  
<a href="https://github.com/astral-sh/ruff"><img alt="Code style: ruff" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json"></a>
</div>

> Detect and segment 40 classes in MRI and CT of the abdominal / pelvic / thorax region.
> Now with 10 additional body composition classes in MRI


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. 


### Updates (v2.0.0)
- We added 10 additional body composition classes, exclusively for MRI. You can segment them by setting `--body_comp`
- You can accelerate segmentation with the `--fast` flag
 

![Sample Image](images/SampleSegmentation.png)

## Installation
Install MRSegmentator with pip:
```bash
# Create virtual environment
conda create -n mrseg python=3.11 pip
conda activate mrseg

# Install MRSegmentator
python -m pip install mrsegmentator
```
If the installed pytorch version is not compatible to your system, you might need to install it manually. Please refer to [PyTorch](https://pytorch.org/get-started/locally/). MRSegmentator requires torch <= 2.3.1.

## Docker Image
You can run an MRSegmentator (v1.2) Docker image directly from [MHub](https://mhub.ai/models/mrsegmentator).
```bash
$input_dir=/path/to/input
$output_dir=/path/to/output

docker run --rm -t --gpus all --network=none -v $input_dir:/app/data/input_data:ro -v $output_dir:/app/data/output_data mhubai/mrsegmentator:latest --workflow default
```


## Inference
MRSegmentator segments all `.nii/.nii.gz/.mha/.nrrd` 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 `--fast`.
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](evaluation) section. 

You can now also run MRSegmentator on DICOM directories, in which case it produces a  DICOM SEG. (Make sure that there is only a single series UID in the directory). You can also convert previously created segmentations back to DICOM SEG (see [dcm_helper](DCM_Helper_README.md)).

```bash
mrsegmentator --input <file / directory / DICOM directory>
```

Options:
```bash
-i, --input <str> [required] # input directory or file

-o --outdir <str>   # output directory
--body_comp         # segment 10 body composition classes in MRI
--fast              # accelerate segmentation by disabling ensembling, mirroring, and by using a larget step size
--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

# debugging
--log_level <["DEBUG", "INFO", "WARNING", "ERROR"]> # Default: INFO
--no_tqdm               # disable tqdm progress bars

# experimental
--split_margin <int>    # split images with an overlap of 2xmargin to avoid hard cutt-offs between segmentations of top and bottom image, default: 3
--nproc <int>           # number of processes
--nproc_export <int>    # number of processes for exporting the segmentations
--fold <int>            # specify a single fold for segmentation
```

## Python API
```python
from mrsegmentator import inference
import os

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

inference.infer(images, outdir, model_name="base")
```

## Manage Weights
Weights are automatically downloaded to `~/.mrsegmentator`. If you want to specify a custom weights directory you can do so by setting an environmental variable: `export MRSEG_WEIGHTS_PATH=<path>`.

You can also use MRSegmentator as an interface to run other nnunetv2 models by pointing the MRSEG_WEIGHTS_PATH variable directly to the nnunetv2 folder with the plans.json file. By doing so you have a nice inference interface with all the included pre-processing:

 - force LPS orientation during loading
 - support for various file types
 - fast mode (--fast) and memory efficiency (--split_level 2)

## How To Cite
If you use our work in your research, please cite our article: https://doi.org/10.1148/ryai.240777.

If you use the body composition classes, please additionally cite this preprint: https://doi.org/10.1101/2025.06.03.25328867

## Class details

![Sample Image](images/Anatomy_40_classes.png)

|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 |

### Body Composition (Exclusively MRI)

| Index | Class |
| :--- | :--- |
| 1 | subcutaneous_fat |
| 2 | visceral_fat |
| 3 | left_rectus_abdominis |
| 4 | right_rectus_abdominis |
| 5 | left_oblique_muscle |
| 6 | right_oblique_muscle |
| 7 | left_quadratus_lumborum |
| 8 | right_quadratus_lumborum |
| 9 | abdominal_subcutaneous_fat |
| 10 | gluteofemoral_fat |





## Testing
The test suite has two modes:

**Smoke tests** — no model weights or real images required, runs in seconds

**Integration tests** — requires model weights and at least one real MRI/CT image

```bash
make smoke

cp tests/mrsegmentator/integration_config.example.py tests/mrsegmentator/integration_config.py
#fill in MODEL_PATH and TEST_IMAGES
make full
```

Integration tests run inference exactly once and validate that outputs have correct labels, geometry, and are readable. 
Slice figures (axial / coronal / sagittal) are saved to `reports/figures/` after each run.


##  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.

![Funding Statement](images/eu_funding_statement.png)
# Changelog

<!--next-version-placeholder-->


## v2.0.0 (06/07/2027)

### Feature
- 10 additional classes for body composition in MRI
- weights management supports muliple weights
- dedicatated weights directory as ~/.mrsegmentator
- env variable MRSEG_WEIGHTS_PATH allows to link to a custom weights dir
- if MRSEG_WEIGHTS_PATH points to any nnUNet results direcotry, these weights can be loaded as well
- More CI tests, with full integration workflows
- More GitHub action workflows
- Updated supported python versions to 3.10-3-13. (3.9 is not working)
- Added fast inference mode with `--fast`


## v1.3.2 (26/05/2026)

### Fix
- Fixed incorrect handling of image orientations such as PIL or ASL

## v1.3.1 (11/08/2025)

### Fix
- fixed incorrect file names that occured when mapping DICOM to NIfTI

## v1.3.0 (09/08/2025)

### Feature
- Automatically run on CPU if no GPU was detected
- Add support for .mha and .nrrd
- Add support for DICOM and DICOM SEG
- Enable better logging:
    - control log lebel (DEBUG, INFO, WARNING)
    - add flag --no_tqdm to disable progress bars
    - remove --verbose flag (replaced by --log_level DEBUG)

## v1.2.3 (05/02/2025)

### Feature
- Print image and subvolume size if splitting is used
- Add option --split_margin to allow to change overlap between splitted volumes

### Fix
- Set pytotch version to <= 2.3.1
- Set python version to < 3.13
- ==> Fixes toch.pickle error due to updated dependency- Supress torch.load future warning, introduced by nnunet
- Increase default split_margin from 2 to 3

## v1.2.2 (11/12/2024)

### Feature
- Print segmentation time after finishing

### Fix
- Supress torch.load future warning, introduced by nnunet
- Print version number of custom weight directories, if they are specified

## 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
