Getting started

import torch
import torchio as tio

# Each instance of tio.Subject is passed arbitrary keyword arguments.
# Typically, these arguments will be instances of tio.Image
subject_a = tio.Subject(
    t1=tio.ScalarImage('subject_a.nii.gz'),
    label=tio.LabelMap('subject_a.nii'),
    diagnosis='positive',
    age=36,
)

# Image files can be in any format supported by SimpleITK or NiBabel, including DICOM
subject_b = tio.Subject(
    t1=tio.ScalarImage('subject_b_dicom_folder/'),
    label=tio.LabelMap('subject_b_seg.nrrd'),
    diagnosis='negative',
    age=24,
)

# Images may also be created using PyTorch tensors or NumPy arrays
tensor_4d = torch.rand(4, 100, 100, 100)
subject_c = tio.Subject(
    t1=tio.ScalarImage(tensor=tensor_4d),
    label=tio.LabelMap(tensor=(tensor_4d > 0.5)),
    diagnosis='negative',
    age=19,
)

subjects_list = [
    subject_a,
    subject_b,
    subject_c,
]

# Let's use one preprocessing transform and one augmentation transform
# This transform will be applied only to scalar images:
rescale = tio.RescaleIntensity(out_min_max=(0, 1))

# As RandomAffine is faster then RandomElasticDeformation, we choose to
# apply RandomAffine 80% of the times and RandomElasticDeformation the rest
# Also, there is a 25% chance that none of them will be applied
spatial = tio.OneOf({
        tio.RandomAffine(): 0.8,
        tio.RandomElasticDeformation(): 0.2,
    },
    p=0.75,
)

# Transforms can be composed as in torchvision.transforms
transforms = [rescale, spatial]
transform = tio.Compose(transforms)

# SubjectsDataset is a subclass of torch.data.utils.Dataset
subjects_dataset = tio.SubjectsDataset(subjects_list, transform=transform)

# Images are processed in parallel thanks to a SubjectsLoader
# (which inherits from torch.utils.data.DataLoader)
training_loader = tio.SubjectsLoader(
    subjects_dataset,
    batch_size=4,
    num_workers=4,
    shuffle=True,
)

# Training epoch
for subjects_batch in training_loader:
    inputs = subjects_batch['t1'][tio.DATA]
    target = subjects_batch['label'][tio.DATA]