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-3D

Simple 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, resize_type=0, 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=(80, 120), p=0.2),
    ], p=1.0)

aug = get_augmentation((64, 128, 128))

img = np.random.randint(0, 255, size=(128, 256, 256), dtype=np.uint8)
lbl = np.random.randint(0, 1, size=(128, 256, 256), dtype=np.uint8)

# 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
ColorJitter

Speed table

Speed in seconds per one sample.

Aug name	Cube = 64px	Cube = 96px	Cube = 128px	Cube = 224px	Cube = 256px
Rotate	0.0402	0.1366	0.3246	1.7546	2.6349
RandomCropFromBorders	0.0037	0.0129	0.0315	0.1634	0.2426
ElasticTransform	0.1588	0.5439	2.8649	11.8937	42.3886
Resize (type = 0)	0.4029	0.4077	0.4245	0.5545	0.6278
Resize (type = 1)	0.3618	0.3696	0.3871	0.5174	0.5896
Flip	0.0042	0.0134	0.0314	0.1649	0.2453
RandomRotate90	0.0040	0.0140	0.0306	0.1672	0.2439
GaussianNoise	0.0143	0.0406	0.0956	0.4992	0.7381
RandomGamma	0.0066	0.0211	0.0505	0.2654	0.3989
RandomScale	0.0158	0.0518	0.1198	0.6391	0.9457

Citation

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}
}