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 may also be of interest.

Class: brats.AdultGliomaPreTreatmentSegmenter (Docs)
Challenge Paper 2023: Link

from brats import AdultGliomaPostTreatmentSegmenter
from brats.constants import AdultGliomaPostTreatmentAlgorithms

segmenter = AdultGliomaPostTreatmentSegmenter(algorithm=AdultGliomaPostTreatmentAlgorithms.BraTS23_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",
)

Class: brats.AdultGliomaPostTreatmentSegmenter (Docs)
Challenge Paper 2024: Link

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)
Challenge Paper 2023 Link
Challenge Paper 2024: N/A

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.

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)
Challenge Paper 2024 Link
Challenge Paper 2023 Link

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 may also be of interest.

Class: brats.MetastasesSegmenter (Docs)
Challenge Paper 2023 Link

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 may also be of interest.

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)
Challenge Paper 2024: N/A

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)
Challenge Paper 2023 and 2024 Link

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)
Challenge Paper 2024: N/A