Volumentations 3D
3D Volume data augmentation package inspired by albumentations.
Volumentations is a working project, which originated from the following Git repositories:
- Original: https://github.com/albumentations-team/albumentations
- 3D Conversion: https://github.com/ashawkey/volumentations
- Continued Development: https://github.com/ZFTurbo/volumentations
Nevertheless, if you are using this subpackage, please give credit to all authors including ashawkey, ZFTurbo, qubvel and muellerdo.
Initially inspired by albumentations library for augmentation of 2D images.
Installation
pip install volumentations-3DSimple Example
from volumentations import *
def get_augmentation(patch_size):
return Compose([
Rotate((-15, 15), (0, 0), (0, 0), p=0.5),
RandomCropFromBorders(crop_value=0.1, p=0.5),
ElasticTransform((0, 0.25), interpolation=2, p=0.1),
Resize(patch_size, interpolation=1, always_apply=True, p=1.0),
Flip(0, p=0.5),
Flip(1, p=0.5),
Flip(2, p=0.5),
RandomRotate90((1, 2), p=0.5),
GaussianNoise(var_limit=(0, 5), p=0.2),
RandomGamma(gamma_limit=(0.5, 1.5), p=0.2),
], p=1.0)
aug = get_augmentation((64, 128, 128))
# with mask
data = {'image': img, 'mask': lbl}
aug_data = aug(**data)
img, lbl = aug_data['image'], aug_data['mask']
# without mask
data = {'image': img}
aug_data = aug(**data)
img = aug_data['image']- Check working usage example in tst_volumentations_type_1.py
- Added another usage example / testing in tst_volumentations_type_2.py
Difference from initial version
- Diverse bug fixes.
- Implemented multiple augmentations.
- Approximation enhancements to be closer to Albumentations.
Implemented 3D augmentations
PadIfNeeded
GaussianNoise
Resize
RandomScale
RotatePseudo2D
RandomRotate90
Flip
Normalize
Float
Contiguous
Transpose
CenterCrop
RandomResizedCrop
RandomCrop
CropNonEmptyMaskIfExists
ResizedCropNonEmptyMaskIfExists
RandomGamma
ElasticTransformPseudo2D
ElasticTransform
Rotate
RandomCropFromBorders
GridDropout
RandomDropPlane
RandomBrightnessContrast
ColorJitterCitation
For more details, please refer to the publication: https://doi.org/10.1016/j.compbiomed.2021.105089
If you find this code useful, please cite it as:
@article{solovyev20223d,
title={3D convolutional neural networks for stalled brain capillary detection},
author={Solovyev, Roman and Kalinin, Alexandr A and Gabruseva, Tatiana},
journal={Computers in Biology and Medicine},
volume={141},
pages={105089},
year={2022},
publisher={Elsevier},
doi={10.1016/j.compbiomed.2021.105089}
}
