# BraTS Orchestrator
[](https://pypi.org/project/brats/)
[](https://pypi.python.org/pypi/brats/)
[](http://brats.readthedocs.io/?badge=latest)
[](https://github.com/BrainLesion/brats/actions/workflows/tests.yml)
[](https://codecov.io/gh/BrainLesion/BraTS)
[](https://opensource.org/licenses/Apache-2.0)
Providing the top-performing algorithms from the Brain Tumor Segmentation (BraTS) challenges, through an easy-to-use Python API powered by Docker.
## Features
- Access to top-performing algorithms from recent BraTS challenges
- Easy-to-use minimal API
- Extensive documentation and examples
## Installation
With a Python 3.8+ environment, you can install BraTS orchestrator directly from [PyPI](https://pypi.org/project/brats/):
```bash
pip install brats
```
> [!IMPORTANT]
> To run BraTS orchestrator, you require a Docker installation. <br>
> Many algorithms also require GPU support (NVIDIA Docker). <br>
> In case you do not have access to a CUDA-capable GPU, the overview tables in the [Available Algorithms and Usage](#available-algorithms-and-usage) section indicate which algorithms are CPU compatible.
### Docker and NVIDIA Container Toolkit Setup
- **Docker**: Installation instructions on the official [website](https://docs.docker.com/get-docker/)
- **NVIDIA Container Toolkit**: Refer to the [NVIDIA install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and the official [GitHub page](https://github.com/NVIDIA/nvidia-container-toolkit)
## Available Algorithms and Usage
### Segmentation Challenges
<img src="https://github.com/BrainLesion/brats/blob/main/figures/segmentation_fig.png?raw=true" alt="matched_instance_figure" height="250"/>
#### Adult Glioma Segmentation (Pre-Treatment)
> Adult Glioma Segmentation on pre-treatment brain MRI exams.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import AdultGliomaPreTreatmentSegmenter
from brats.constants import AdultGliomaPreTreatmentAlgorithms
segmenter = AdultGliomaPreTreatmentSegmenter(algorithm=AdultGliomaPreTreatmentAlgorithms.BraTS23_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm of 2023 will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
> Note: If you're interested in Adult Glioma Segmentation, the [BrainLes GlioMODA package](https://github.com/BrainLesion/GlioMODA?tab=readme-ov-file#gliomoda) may also be of interest.
<br>
**Class:** `brats.AdultGliomaPreTreatmentSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AdultGliomaPreTreatmentSegmenter))
<br>
**Challenge Paper 2023:** [Link](https://arxiv.org/abs/2107.02314)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | --------------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| 2023 | 1st | _André Ferreira, et al._ | [Link](https://arxiv.org/abs/2402.17317v1) | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_1) |
| 2023 | 2nd | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_2) |
| 2023 | 3rd | _Fadillah Adamsyah Maani, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_24) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_3) |
</details>
<br>
#### Adult Glioma Segmentation (Post-Treatment)
> Adult Glioma Segmentation on post-Treatment brain MRI exams.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import AdultGliomaPostTreatmentSegmenter
from brats.constants import AdultGliomaPostTreatmentAlgorithms
segmenter = AdultGliomaPostTreatmentSegmenter(algorithm=AdultGliomaPostTreatmentAlgorithms.BraTS24_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm of 2024 will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
<br>
**Class:** `brats.AdultGliomaPostTreatmentSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AdultGliomaPostTreatmentSegmenter))
<br>
**Challenge Paper 2024:** [Link](https://doi.org/10.48550/arXiv.2405.18368)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | ------------------------ | ---------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _André Ferreira, et al._ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_1) |
| 2024 | 2nd | _Heejong Kim, et al._ | [Link](https://arxiv.org/abs/2409.08143) | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Adrian Celaya_ | N/A | ✅ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_3) |
</details>
<br>
#### BraTS-Africa Segmentation
> Adult Glioma Segmentation on brain MRI exams in Sub-Sahara-Africa patient population.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import AfricaSegmenter
from brats.constants import AfricaAlgorithms
segmenter = AfricaSegmenter(algorithm=AfricaAlgorithms.BraTS23_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
**Class:** `brats.AfricaSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AfricaSegmenter))
<br>
**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.19369)
<br>
**Challenge Paper 2024**: N/A
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | -------------------------- | ---------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _Zhifan Jiang et al._ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_1) |
| 2024 | 2nd | _Long Bai, et al._ | N/A | ✅ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Sarim Hashmi, et al._ | [Link](https://doi.org/10.48550/arXiv.2411.15872) | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_3) |
| 2023 | 1st | _Andriy Myronenko, et al._ | TODO | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_1) |
| 2023 | 2nd | _Alyssa R Amod, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_22) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_2) |
| 2023 | 3rd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_3) |
</details>
<br>
#### Meningioma Segmentation
> Segmentation of Meningioma on brain MRI exams.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
Unlike other segmentation challenges, the expected inputs for the Meningioma Segmentation Algorithms differ between years.
- _2023_: All 4 modalities are used (t1c, t1n, t2f, t2w)
- _2024_: Only t1c is used
Therefore, the usage differs slightly, depending on which algorithm is used. To understand why, please refer to the [2024 challenge manuscript](https://arxiv.org/abs/2405.18383).
```python
from brats import MeningiomaSegmenter
from brats.constants import MeningiomaAlgorithms
### Example for 2023 algorithms
segmenter = MeningiomaSegmenter(algorithm=MeningiomaAlgorithms.BraTS23_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation_23.nii.gz",
)
### Example for 2024 algorithms
segmenter = MeningiomaSegmenter(algorithm=MeningiomaAlgorithms.BraTS24_1, cuda_devices="0")
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
output_file="segmentation_24.nii.gz",
)
```
**Class:** `brats.MeningiomaSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.MeningiomaSegmenter))
<br>
**Challenge Paper 2024** [Link](https://arxiv.org/abs/2405.18383)
<br>
**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.07642)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | --------------------------------- | ---------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _Valeria Abramova_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_1) |
| 2024 | 2nd | _Mehdi Astaraki_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Andre Ferreira, et al._ | [Link](https://arxiv.org/html/2411.04632v1) | ✅ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_3) |
| 2023 | 1st | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_1) |
| 2023 | 2nd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_2) |
| 2023 | 3rd | _Daniel Capell'an-Mart'in et al._ | [Link](https://api.semanticscholar.org/CorpusID:272599903) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_3) |
</details>
<br>
#### Brain Metastases Segmentation
> Segmentation on brain metastases on MRI exams for pre- and post-treatment cases.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import MetastasesSegmenter
from brats.constants import MetastasesAlgorithms
segmenter = MetastasesSegmenter(algorithm=MetastasesAlgorithms.BraTS23_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
> Note: If you're interested in Brain Metastases Segmentation, the [BrainLes AURORA package](https://github.com/BrainLesion/AURORA#aurora) may also be of interest.
<br>
**Class:** `brats.MetastasesSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.MetastasesSegmenter))
<br>
**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2306.00838)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | -------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |
| 2023 | 1st | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_1) |
| 2023 | 2nd | _Siwei Yang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_17) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_2) |
| 2023 | 3rd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_3) |
</details>
<br>
#### Pediatric Segmentation
> Segmentation of pediatric brain tumors on MRI exams.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import PediatricSegmenter
from brats.constants import PediatricAlgorithms
segmenter = PediatricSegmenter(algorithm=PediatricAlgorithms.BraTS23_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
> Note: If you're interested in Pediatric Segmentation, the [BrainLes PeTu package](https://github.com/BrainLesion/PeTu?tab=readme-ov-file#petu) may also be of interest.
<br>
**Class:** `brats.PediatricSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.PediatricSegmenter))
<br>
**Challenge Paper 2024** [Link](https://doi.org/10.48550/arXiv.2404.15009)
<br>
**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.17033)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | -------------------------- | ---------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------ |
| 2024 | 1st | _Tim Mulvany, et al._ | [Link](https://doi.org/10.48550/arXiv.2410.14020) | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_1) |
| 2024 | 2nd | _Mehdi Astaraki_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Sarim Hashmi, et al._ | [Link](https://doi.org/10.48550/arXiv.2411.15872) | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_3) |
| 2023 | 1st | _Zhifan Jiang et al._ | [Link](https://api.semanticscholar.org/CorpusID:272599903) | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_1) |
| 2023 | 2nd | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_2) |
| 2023 | 3rd | _Yubo Zhou_ | [Link](https://doi.org/10.1007/978-3-031-76163-8_5) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_3) |
</details>
<br>
#### Generalizability Across Tumors (BraTS-GoAT) Segmentation
> Segmentation algorithm, adapting and generalizing to different brain tumors with segmentation labels of different tumor sub-regions.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import GoATSegmenter
from brats.constants import GoATAlgorithms
segmenter = GoATSegmenter(algorithm=GoATAlgorithms.BraTS24_1, cuda_devices="0")
# these parameters are optional, by default the winning algorithm will be used on cuda:0
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
```
**Class:** `brats.PediatricSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.PediatricSegmenter))
<br>
**Challenge Paper 2024:** N/A
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | -------------------------- | ----- | ----------- | ------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _Frank Miao, Shengjie Niu_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.GoATAlgorithms.BraTS24_1) |
</details>
<br>
### Inpainting Challenge
<img src="https://github.com/BrainLesion/brats/blob/main/figures/inpainting_fig.png?raw=true" alt="matched_instance_figure" height="250"/>
> Algorithm to realistically synthesize and fill 3D healthy brain tissue in a region affected by glioma in brain MRI exams.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import Inpainter
from brats.constants import InpaintingAlgorithms
inpainter = Inpainter(algorithm=InpaintingAlgorithms.BraTS24_1, cuda_devices="0")
inpainter.infer_single(
t1n="path/to/voided_t1n.nii.gz",
mask="path/to/mask.nii.gz",
output_file="inpainting.nii.gz",
)
```
**Class:** `brats.Inpainter` ([Docs](https://brats.readthedocs.io/en/latest/core/inpainting_algorithms.html#brats.core.inpainting_algorithms.Inpainter))
<br>
**Challenge Paper 2023 and 2024** [Link](https://arxiv.org/pdf/2305.08992)
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | ---------------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _Ke Chen, Juexin Zhang, Ying Weng_ | N/A | ✅ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_1) |
| 2024 | 2nd | _André Ferreira, et al._ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Alicia Durrer, et al._ | N/A | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_3) |
| 2023 | 1st | _Juexin Zhang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_21) | ✅ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_1) |
| 2023 | 2nd | _Alicia Durrer, et al._ | [Link](https://doi.org/10.48550/arXiv.2402.17307) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_2) |
| 2023 | 3rd | _Jiayu Huo, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_1) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_3) |
</details>
<br>
### Missing MRI Challenge
<img src="https://github.com/BrainLesion/brats/blob/main/figures/missingmri_fig.png?raw=true" alt="matched_instance_figure" height="250"/>
> Algorithm to realistically synthesize missing MRI modalities from available sequences to enhance brain tumor segmentation.
<details>
<summary> Usage example (code) and top 3 participants </summary>
<br>
```python
from brats import MissingMRI
from brats.constants import MissingMRIAlgorithms
missing_mri = MissingMRI(algorithm=MissingMRIAlgorithms.BraTS24_1, cuda_devices="0")
# Example to synthesize t2f modality (whichever modality is missing will be inferred)
missing_mri.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
# t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="inferred_t2f.nii.gz",
)
```
**Class:** `brats.MissingMRI` ([Docs](https://brats.readthedocs.io/en/latest/core/missing_mri_algorithms.html#brats.core.missing_mri_algorithms.MissingMRI))
<br>
**Challenge Paper 2024:** N/A
<br>
<br>
| Year | Rank | Author | Paper | CPU Support | Key Enum |
| ---- | ---- | ----------------------------------------- | ----- | ----------- | ------------------------------------------------------------------------------------------------------------------- |
| 2024 | 1st | _Jihoon Cho, Seunghyuck Park, Jinah Park_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_1) |
| 2024 | 2nd | _Haowen Pang_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_2) |
| 2024 | 3rd | _Minjoo Lim, Bogyeong Kang_ | N/A | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_3) |
| 2023 | 1st | _Ivo Baltruschat_ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS23_1) |
</details>
---
> [!TIP]
> For a full notebook example with more details, please check here:
> [](https://nbviewer.org/github/BrainLesion/tutorials/blob/main/BraTS/tutorial.ipynb)
## Citation
> [!IMPORTANT]
> If you use BraTS orchestrator in your research, please cite it to support the development!
Kofler, F., Rosier, M., Astaraki, M., Baid, U., Möller, H., Buchner, J. A., Steinbauer, F., Oswald, E., Rosa, E. de la, Ezhov, I., See, C. von, Kirschke, J., Schmick, A., Pati, S., Linardos, A., Pitarch, C., Adap, S., Rudie, J., Verdier, M. C. de, … Menze, B. (2025). BraTS orchestrator: Democratizing and Disseminating state-of-the-art brain tumor image analysis [arXiv preprint arXiv:2506.13807](https://doi.org/10.48550/arXiv.2506.13807)
```
@misc{kofler2025bratsorchestratordemocratizing,
title={BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis},
author={Florian Kofler and Marcel Rosier and Mehdi Astaraki and Ujjwal Baid and Hendrik Möller and Josef A. Buchner and Felix Steinbauer and Eva Oswald and Ezequiel de la Rosa and Ivan Ezhov and Constantin von See and Jan Kirschke and Anton Schmick and Sarthak Pati and Akis Linardos and Carla Pitarch and Sanyukta Adap and Jeffrey Rudie and Maria Correia de Verdier and Rachit Saluja and Evan Calabrese and Dominic LaBella and Mariam Aboian and Ahmed W. Moawad and Nazanin Maleki and Udunna Anazodo and Maruf Adewole and Marius George Linguraru and Anahita Fathi Kazerooni and Zhifan Jiang and Gian Marco Conte and Hongwei Li and Juan Eugenio Iglesias and Spyridon Bakas and Benedikt Wiestler and Marie Piraud and Bjoern Menze},
year={2025},
eprint={2506.13807},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2506.13807},
}
```
## Contributing
We welcome all kinds of contributions from the community!
### Reporting Bugs, Feature Requests, and Questions
Please open a new issue [here](https://github.com/BrainLesion/BraTS/issues).
### Code contributions
Nice to have you on board! Please have a look at our [CONTRIBUTING.md](CONTRIBUTING.md) file.
Raw data
{
"_id": null,
"home_page": null,
"name": "brats",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "brain tumor, glioma, BraTS, brain lesion, segmentation, synthesis, inpainting",
"author": "Marcel Rosier",
"author_email": "marcel.rosier@tum.de",
"download_url": "https://files.pythonhosted.org/packages/8e/ca/939e58f4cd46f9041b51171f772b5b2adf7645fd94a4342b5d0199a761c7/brats-0.0.27.tar.gz",
"platform": null,
"description": "# BraTS Orchestrator\n\n[](https://pypi.org/project/brats/)\n[](https://pypi.python.org/pypi/brats/)\n[](http://brats.readthedocs.io/?badge=latest)\n[](https://github.com/BrainLesion/brats/actions/workflows/tests.yml)\n[](https://codecov.io/gh/BrainLesion/BraTS)\n[](https://opensource.org/licenses/Apache-2.0)\n\nProviding the top-performing algorithms from the Brain Tumor Segmentation (BraTS) challenges, through an easy-to-use Python API powered by Docker.\n\n## Features\n\n- Access to top-performing algorithms from recent BraTS challenges\n- Easy-to-use minimal API\n- Extensive documentation and examples\n\n## Installation\n\nWith a Python 3.8+ environment, you can install BraTS orchestrator directly from [PyPI](https://pypi.org/project/brats/):\n\n```bash\npip install brats\n```\n\n> [!IMPORTANT] \n> To run BraTS orchestrator, you require a Docker installation. <br>\n> Many algorithms also require GPU support (NVIDIA Docker). <br>\n> In case you do not have access to a CUDA-capable GPU, the overview tables in the [Available Algorithms and Usage](#available-algorithms-and-usage) section indicate which algorithms are CPU compatible.\n\n\n\n\n### Docker and NVIDIA Container Toolkit Setup\n\n- **Docker**: Installation instructions on the official [website](https://docs.docker.com/get-docker/)\n- **NVIDIA Container Toolkit**: Refer to the [NVIDIA install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and the official [GitHub page](https://github.com/NVIDIA/nvidia-container-toolkit)\n\n\n## Available Algorithms and Usage\n\n### Segmentation Challenges\n<img src=\"https://github.com/BrainLesion/brats/blob/main/figures/segmentation_fig.png?raw=true\" alt=\"matched_instance_figure\" height=\"250\"/>\n\n\n\n#### Adult Glioma Segmentation (Pre-Treatment) \n> Adult Glioma Segmentation on pre-treatment brain MRI exams. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import AdultGliomaPreTreatmentSegmenter\nfrom brats.constants import AdultGliomaPreTreatmentAlgorithms\n\nsegmenter = AdultGliomaPreTreatmentSegmenter(algorithm=AdultGliomaPreTreatmentAlgorithms.BraTS23_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm of 2023 will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n```\n> Note: If you're interested in Adult Glioma Segmentation, the [BrainLes GlioMODA package](https://github.com/BrainLesion/GlioMODA?tab=readme-ov-file#gliomoda) may also be of interest.\n<br>\n\n**Class:** `brats.AdultGliomaPreTreatmentSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AdultGliomaPreTreatmentSegmenter))\n<br>\n**Challenge Paper 2023:** [Link](https://arxiv.org/abs/2107.02314)\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | --------------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------ |\n| 2023 | 1st | _Andr\u00e9 Ferreira, et al._ | [Link](https://arxiv.org/abs/2402.17317v1) | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_1) |\n| 2023 | 2nd | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_2) |\n| 2023 | 3rd | _Fadillah Adamsyah Maani, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_24) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.**AdultGliomaPreTreatmentAlgorithms**.BraTS23_3) |\n\n</details>\n<br>\n\n#### Adult Glioma Segmentation (Post-Treatment)\n> Adult Glioma Segmentation on post-Treatment brain MRI exams.\n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n\n<br>\n\n\n```python\nfrom brats import AdultGliomaPostTreatmentSegmenter\nfrom brats.constants import AdultGliomaPostTreatmentAlgorithms\n\nsegmenter = AdultGliomaPostTreatmentSegmenter(algorithm=AdultGliomaPostTreatmentAlgorithms.BraTS24_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm of 2024 will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n```\n<br>\n\n**Class:** `brats.AdultGliomaPostTreatmentSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AdultGliomaPostTreatmentSegmenter))\n<br>\n**Challenge Paper 2024:** [Link](https://doi.org/10.48550/arXiv.2405.18368)\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | ------------------------ | ---------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Andr\u00e9 Ferreira, et al._ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Heejong Kim, et al._ | [Link](https://arxiv.org/abs/2409.08143) | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Adrian Celaya_ | N/A | ✅ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AdultGliomaPostTreatmentAlgorithms.BraTS24_3) |\n\n</details>\n<br>\n\n#### BraTS-Africa Segmentation\n> Adult Glioma Segmentation on brain MRI exams in Sub-Sahara-Africa patient population. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import AfricaSegmenter\nfrom brats.constants import AfricaAlgorithms\n\nsegmenter = AfricaSegmenter(algorithm=AfricaAlgorithms.BraTS23_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n```\n\n**Class:** `brats.AfricaSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.AfricaSegmenter))\n<br>\n**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.19369)\n<br>\n**Challenge Paper 2024**: N/A \n<br>\n\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | -------------------------- | ---------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Zhifan Jiang et al._ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Long Bai, et al._ | N/A | ✅ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Sarim Hashmi, et al._ | [Link](https://doi.org/10.48550/arXiv.2411.15872) | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS24_3) |\n| 2023 | 1st | _Andriy Myronenko, et al._ | TODO | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_1) |\n| 2023 | 2nd | _Alyssa R Amod, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_22) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_2) |\n| 2023 | 3rd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.AfricaAlgorithms.BraTS23_3) |\n\n</details>\n<br>\n\n#### Meningioma Segmentation\n> Segmentation of Meningioma on brain MRI exams.\n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\nUnlike other segmentation challenges, the expected inputs for the Meningioma Segmentation Algorithms differ between years. \n- _2023_: All 4 modalities are used (t1c, t1n, t2f, t2w)\n- _2024_: Only t1c is used \n\nTherefore, the usage differs slightly, depending on which algorithm is used. To understand why, please refer to the [2024 challenge manuscript](https://arxiv.org/abs/2405.18383).\n\n```python\nfrom brats import MeningiomaSegmenter\nfrom brats.constants import MeningiomaAlgorithms\n\n### Example for 2023 algorithms\nsegmenter = MeningiomaSegmenter(algorithm=MeningiomaAlgorithms.BraTS23_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation_23.nii.gz\",\n)\n\n### Example for 2024 algorithms\nsegmenter = MeningiomaSegmenter(algorithm=MeningiomaAlgorithms.BraTS24_1, cuda_devices=\"0\")\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n output_file=\"segmentation_24.nii.gz\",\n)\n```\n\n**Class:** `brats.MeningiomaSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.MeningiomaSegmenter))\n<br>\n**Challenge Paper 2024** [Link](https://arxiv.org/abs/2405.18383)\n<br>\n**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.07642)\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | --------------------------------- | ---------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Valeria Abramova_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Mehdi Astaraki_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Andre Ferreira, et al._ | [Link](https://arxiv.org/html/2411.04632v1) | ✅ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS24_3) |\n| 2023 | 1st | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_1) |\n| 2023 | 2nd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_2) |\n| 2023 | 3rd | _Daniel Capell'an-Mart'in et al._ | [Link](https://api.semanticscholar.org/CorpusID:272599903) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MeningiomaAlgorithms.BraTS23_3) |\n\n</details>\n<br>\n\n#### Brain Metastases Segmentation\n> Segmentation on brain metastases on MRI exams for pre- and post-treatment cases. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import MetastasesSegmenter\nfrom brats.constants import MetastasesAlgorithms\n\nsegmenter = MetastasesSegmenter(algorithm=MetastasesAlgorithms.BraTS23_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n\n```\n> Note: If you're interested in Brain Metastases Segmentation, the [BrainLes AURORA package](https://github.com/BrainLesion/AURORA#aurora) may also be of interest.\n<br>\n\n**Class:** `brats.MetastasesSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.MetastasesSegmenter))\n<br>\n**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2306.00838)\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | -------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |\n| 2023 | 1st | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_1) |\n| 2023 | 2nd | _Siwei Yang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_17) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_2) |\n| 2023 | 3rd | _Ziyan Huang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_13) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MetastasesAlgorithms.BraTS23_3) |\n\n</details>\n<br>\n\n#### Pediatric Segmentation\n> Segmentation of pediatric brain tumors on MRI exams. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import PediatricSegmenter\nfrom brats.constants import PediatricAlgorithms\n\nsegmenter = PediatricSegmenter(algorithm=PediatricAlgorithms.BraTS23_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n```\n> Note: If you're interested in Pediatric Segmentation, the [BrainLes PeTu package](https://github.com/BrainLesion/PeTu?tab=readme-ov-file#petu) may also be of interest.\n<br>\n**Class:** `brats.PediatricSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.PediatricSegmenter))\n<br>\n**Challenge Paper 2024** [Link](https://doi.org/10.48550/arXiv.2404.15009)\n<br>\n**Challenge Paper 2023** [Link](https://doi.org/10.48550/arXiv.2305.17033)\n<br>\n\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | -------------------------- | ---------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------ |\n| 2024 | 1st | _Tim Mulvany, et al._ | [Link](https://doi.org/10.48550/arXiv.2410.14020) | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Mehdi Astaraki_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Sarim Hashmi, et al._ | [Link](https://doi.org/10.48550/arXiv.2411.15872) | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS24_3) |\n| 2023 | 1st | _Zhifan Jiang et al._ | [Link](https://api.semanticscholar.org/CorpusID:272599903) | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_1) |\n| 2023 | 2nd | _Andriy Myronenko, et al._ | N/A | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_2) |\n| 2023 | 3rd | _Yubo Zhou_ | [Link](https://doi.org/10.1007/978-3-031-76163-8_5) | ❌ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.PediatricAlgorithms.BraTS23_3) |\n\n</details>\n<br>\n\n#### Generalizability Across Tumors (BraTS-GoAT) Segmentation \n> Segmentation algorithm, adapting and generalizing to different brain tumors with segmentation labels of different tumor sub-regions. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import GoATSegmenter\nfrom brats.constants import GoATAlgorithms\n\nsegmenter = GoATSegmenter(algorithm=GoATAlgorithms.BraTS24_1, cuda_devices=\"0\")\n# these parameters are optional, by default the winning algorithm will be used on cuda:0\nsegmenter.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"segmentation.nii.gz\",\n)\n```\n\n**Class:** `brats.PediatricSegmenter` ([Docs](https://brats.readthedocs.io/en/latest/core/segmentation_algorithms.html#brats.core.segmentation_algorithms.PediatricSegmenter))\n<br>\n**Challenge Paper 2024:** N/A\n<br> \n\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | -------------------------- | ----- | ----------- | ------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Frank Miao, Shengjie Niu_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.GoATAlgorithms.BraTS24_1) |\n\n</details>\n<br>\n\n### Inpainting Challenge \n<img src=\"https://github.com/BrainLesion/brats/blob/main/figures/inpainting_fig.png?raw=true\" alt=\"matched_instance_figure\" height=\"250\"/>\n\n\n> Algorithm to realistically synthesize and fill 3D healthy brain tissue in a region affected by glioma in brain MRI exams. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n\n```python\nfrom brats import Inpainter\nfrom brats.constants import InpaintingAlgorithms\n\ninpainter = Inpainter(algorithm=InpaintingAlgorithms.BraTS24_1, cuda_devices=\"0\")\ninpainter.infer_single(\n t1n=\"path/to/voided_t1n.nii.gz\",\n mask=\"path/to/mask.nii.gz\",\n output_file=\"inpainting.nii.gz\",\n)\n```\n\n**Class:** `brats.Inpainter` ([Docs](https://brats.readthedocs.io/en/latest/core/inpainting_algorithms.html#brats.core.inpainting_algorithms.Inpainter))\n<br>\n**Challenge Paper 2023 and 2024** [Link](https://arxiv.org/pdf/2305.08992)\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | ---------------------------------- | ---------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Ke Chen, Juexin Zhang, Ying Weng_ | N/A | ✅ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Andr\u00e9 Ferreira, et al._ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Alicia Durrer, et al._ | N/A | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS24_3) |\n| 2023 | 1st | _Juexin Zhang, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_21) | ✅ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_1) |\n| 2023 | 2nd | _Alicia Durrer, et al._ | [Link](https://doi.org/10.48550/arXiv.2402.17307) | ❌ | [BraTS23_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_2) |\n| 2023 | 3rd | _Jiayu Huo, et al._ | [Link](https://doi.org/10.1007/978-3-031-76163-8_1) | ✅ | [BraTS23_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.InpaintingAlgorithms.BraTS23_3) |\n\n</details>\n<br>\n\n### Missing MRI Challenge\n<img src=\"https://github.com/BrainLesion/brats/blob/main/figures/missingmri_fig.png?raw=true\" alt=\"matched_instance_figure\" height=\"250\"/>\n\n\n> Algorithm to realistically synthesize missing MRI modalities from available sequences to enhance brain tumor segmentation. \n<details>\n<summary> Usage example (code) and top 3 participants </summary>\n<br>\n\n```python\nfrom brats import MissingMRI\nfrom brats.constants import MissingMRIAlgorithms\n\nmissing_mri = MissingMRI(algorithm=MissingMRIAlgorithms.BraTS24_1, cuda_devices=\"0\")\n# Example to synthesize t2f modality (whichever modality is missing will be inferred)\nmissing_mri.infer_single(\n t1c=\"path/to/t1c.nii.gz\",\n t1n=\"path/to/t1n.nii.gz\",\n # t2f=\"path/to/t2f.nii.gz\",\n t2w=\"path/to/t2w.nii.gz\",\n output_file=\"inferred_t2f.nii.gz\",\n)\n```\n\n**Class:** `brats.MissingMRI` ([Docs](https://brats.readthedocs.io/en/latest/core/missing_mri_algorithms.html#brats.core.missing_mri_algorithms.MissingMRI))\n<br>\n**Challenge Paper 2024:** N/A\n<br>\n<br>\n| Year | Rank | Author | Paper | CPU Support | Key Enum |\n| ---- | ---- | ----------------------------------------- | ----- | ----------- | ------------------------------------------------------------------------------------------------------------------- |\n| 2024 | 1st | _Jihoon Cho, Seunghyuck Park, Jinah Park_ | N/A | ❌ | [BraTS24_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_1) |\n| 2024 | 2nd | _Haowen Pang_ | N/A | ❌ | [BraTS24_2](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_2) |\n| 2024 | 3rd | _Minjoo Lim, Bogyeong Kang_ | N/A | ❌ | [BraTS24_3](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS24_3) |\n| 2023 | 1st | _Ivo Baltruschat_ | N/A | ❌ | [BraTS23_1](https://brats.readthedocs.io/en/latest/utils/utils.html#brats.constants.MissingMRIAlgorithms.BraTS23_1) |\n\n</details>\n\n--- \n\n> [!TIP]\n> For a full notebook example with more details, please check here: \n> [](https://nbviewer.org/github/BrainLesion/tutorials/blob/main/BraTS/tutorial.ipynb)\n\n## Citation\n\n> [!IMPORTANT]\n> If you use BraTS orchestrator in your research, please cite it to support the development!\n\nKofler, F., Rosier, M., Astaraki, M., Baid, U., M\u00f6ller, H., Buchner, J. A., Steinbauer, F., Oswald, E., Rosa, E. de la, Ezhov, I., See, C. von, Kirschke, J., Schmick, A., Pati, S., Linardos, A., Pitarch, C., Adap, S., Rudie, J., Verdier, M. C. de, \u2026 Menze, B. (2025). BraTS orchestrator: Democratizing and Disseminating state-of-the-art brain tumor image analysis [arXiv preprint arXiv:2506.13807](https://doi.org/10.48550/arXiv.2506.13807)\n\n\n```\n@misc{kofler2025bratsorchestratordemocratizing,\n title={BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis}, \n author={Florian Kofler and Marcel Rosier and Mehdi Astaraki and Ujjwal Baid and Hendrik M\u00f6ller and Josef A. Buchner and Felix Steinbauer and Eva Oswald and Ezequiel de la Rosa and Ivan Ezhov and Constantin von See and Jan Kirschke and Anton Schmick and Sarthak Pati and Akis Linardos and Carla Pitarch and Sanyukta Adap and Jeffrey Rudie and Maria Correia de Verdier and Rachit Saluja and Evan Calabrese and Dominic LaBella and Mariam Aboian and Ahmed W. Moawad and Nazanin Maleki and Udunna Anazodo and Maruf Adewole and Marius George Linguraru and Anahita Fathi Kazerooni and Zhifan Jiang and Gian Marco Conte and Hongwei Li and Juan Eugenio Iglesias and Spyridon Bakas and Benedikt Wiestler and Marie Piraud and Bjoern Menze},\n year={2025},\n eprint={2506.13807},\n archivePrefix={arXiv},\n primaryClass={eess.IV},\n url={https://arxiv.org/abs/2506.13807}, \n}\n```\n\n## Contributing\n\nWe welcome all kinds of contributions from the community!\n\n### Reporting Bugs, Feature Requests, and Questions\n\nPlease open a new issue [here](https://github.com/BrainLesion/BraTS/issues).\n\n### Code contributions\n\nNice to have you on board! Please have a look at our [CONTRIBUTING.md](CONTRIBUTING.md) file.\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "BraTS algorithms",
"version": "0.0.27",
"project_urls": {
"Documentation": "https://brats.readthedocs.io/en/latest/",
"Homepage": "https://github.com/BrainLesion/BraTS",
"Repository": "https://github.com/BrainLesion/BraTS"
},
"split_keywords": [
"brain tumor",
" glioma",
" brats",
" brain lesion",
" segmentation",
" synthesis",
" inpainting"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "de784d667a8cd11438bef6ec5fdd9e13c5834423b795ddcb5038a2fe7fa47e86",
"md5": "4f7c6863e0ca9795582ca0e356ccd9b9",
"sha256": "7babf562082fc932290ffe0cbf1bed1a362b3f758749d1f50443859291ed39e4"
},
"downloads": -1,
"filename": "brats-0.0.27-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4f7c6863e0ca9795582ca0e356ccd9b9",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 44984,
"upload_time": "2025-07-25T14:09:30",
"upload_time_iso_8601": "2025-07-25T14:09:30.480730Z",
"url": "https://files.pythonhosted.org/packages/de/78/4d667a8cd11438bef6ec5fdd9e13c5834423b795ddcb5038a2fe7fa47e86/brats-0.0.27-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "8eca939e58f4cd46f9041b51171f772b5b2adf7645fd94a4342b5d0199a761c7",
"md5": "8915c5f98773cd79bfcf2b983e321828",
"sha256": "37c5128c68104d260ed0ce523a6c904ad6c71605203d11a9257c0fffd8bca472"
},
"downloads": -1,
"filename": "brats-0.0.27.tar.gz",
"has_sig": false,
"md5_digest": "8915c5f98773cd79bfcf2b983e321828",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 35424,
"upload_time": "2025-07-25T14:09:31",
"upload_time_iso_8601": "2025-07-25T14:09:31.947744Z",
"url": "https://files.pythonhosted.org/packages/8e/ca/939e58f4cd46f9041b51171f772b5b2adf7645fd94a4342b5d0199a761c7/brats-0.0.27.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-25 14:09:31",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "BrainLesion",
"github_project": "BraTS",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "brats"
}
JSON
