MONAI is an open-source framework for medical imaging AI, developed by a consortium of leading research institutions and industry partners. The framework provides a comprehensive set of tools and libraries for building, training, and deploying AI models in medical imaging. MONAI is designed to facilitate the development of accurate and robust models for various medical imaging applications, including image segmentation, detection, and classification.
| Transform | Description | |-----------|-------------| | RandSimulateLowResolution | Reduce effective resolution | | RandSpacing | Randomly change pixel spacing (simulate different acquisition grids) |
train_transform = Compose([ LoadImaged(keys=["image", "label"]), EnsureChannelFirstd(keys=["image", "label"]), ScaleIntensityRanged(keys=["image"], a_min=-1000, a_max=1000), RandRotated(keys=["image", "label"], prob=0.5, range_x=0.3), RandZoomd(keys=["image", "label"], prob=0.3, min_zoom=0.8, max_zoom=1.2), RandGaussianNoised(keys=["image"], prob=0.4, std=0.05), ToTensord(keys=["image", "label"]) ]) monai data augmentation
| Transform | Description | |-----------|-------------| | RandCoarseShuffle | Randomly shuffle patches across channels (simulate missing modality) | | RandChannelShuffle | Shuffle MRI sequences (T1, T2, FLAIR) |
from monai.transforms import RandRotate, RandZoom, RandGaussianNoise, OneOf, RandomOrder MONAI is an open-source framework for medical imaging
By staying up-to-date with the latest developments in MONAI data augmentation, researchers and developers can unlock the full potential of medical imaging AI and improve patient outcomes.
Modify geometry while preserving anatomical plausibility. FLAIR) | from monai.transforms import RandRotate
from monai.transforms import ( Compose, LoadImaged, AddChanneld, Spacingd, RandCropByPosNegLabeld, RandAffined, RandGaussianNoised )
for batch in loader: images, labels = batch["image"], batch["label"] # Training step...
