monai.md

MONAI interoperability

TorchIO and MONAI can work together in both directions.

Use MONAI transforms in TorchIO

Use MonaiAdapter to wrap any MONAI transform for use inside TorchIO pipelines.

Installation

pip install "torchio[monai]"

Array transforms

Array transforms (e.g., NormalizeIntensity) are applied to each ScalarImage in the subject individually:

from monai.transforms import NormalizeIntensity
import torchio as tio

adapter = tio.MonaiAdapter(NormalizeIntensity())
result = adapter(subject)

Use include / exclude to control which images are affected:

adapter = tio.MonaiAdapter(NormalizeIntensity(), include=["t1"])

Dictionary transforms

Dictionary transforms (e.g., NormalizeIntensityd) operate on the full subject dictionary. Only the keys specified in the MONAI transform are modified:

from monai.transforms import NormalizeIntensityd

adapter = tio.MonaiAdapter(NormalizeIntensityd(keys=["t1"]))
result = adapter(subject)

Spatial dictionary transforms (e.g., RandSpatialCropd) propagate affine changes back to the TorchIO images automatically.

Inside a pipeline

MonaiAdapter works in Compose like any other transform:

pipeline = tio.Compose([
    tio.Flip(axes=(0,), p=0.5),
    tio.MonaiAdapter(NormalizeIntensity()),
    tio.Noise(std=(0.01, 0.05)),
])
result = pipeline(subject)

!!! note MonaiAdapter does not record itself in the subject's transform history, because MONAI transform objects are not serializable.

Use TorchIO transforms in MONAI

TorchIO transforms accept dict[str, Tensor] directly, so they work in MONAI dict-based pipelines without any adapter:

from monai.transforms import Compose as MonaiCompose
from monai.transforms import NormalizeIntensityd
import torchio as tio

pipeline = MonaiCompose([
    NormalizeIntensityd(keys=["image"]),
    tio.Noise(std=0.1),             # works on dicts
    tio.Flip(axes=(0,), p=0.5),     # works on dicts
])

data = {"image": tensor, "label": label_tensor}
result = pipeline(data)