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
• Enhancements: https://github.com/qubvel/volumentations

Due to a stop of ongoing development in this subpackage, we decided to create a new repository:
https://github.com/muellerdo/volumentations

Nevertheless, if you are using this subpackage, please give credit to all authors including ashawkey, ZFTurbo and qubvel.

Initially inspired by albumentations library for augmentation of 2D images.

Installation

# Original
pip install volumentations-3D

# Fork
pip install volumentations-aucmedi

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, 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.py
Added another usage example / testing in testing.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

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}
}

Credits and License

Added some credits/license to each file.

#=================================================================================#
#  Author:       Pavel Iakubovskii, ZFTurbo, ashawkey, Dominik Müller             #
#  Copyright:    albumentations:    : https://github.com/albumentations-team      #
#                Pavel Iakubovskii  : https://github.com/qubvel                   #
#                ZFTurbo            : https://github.com/ZFTurbo                  #
#                ashawkey           : https://github.com/ashawkey                 #
#                Dominik Müller     : https://github.com/muellerdo                #
#                                                                                 #
#  Volumentations History:                                                        #
#       - Original:                 https://github.com/albumentations-team/album  #
#                                   entations                                     #
#       - 3D Conversion:            https://github.com/ashawkey/volumentations    #
#       - Continued Development:    https://github.com/ZFTurbo/volumentations     #
#       - Enhancements:             https://github.com/qubvel/volumentations      #
#       - Further Enhancements:     https://github.com/muellerdo/volumentations   #
#                                                                                 #
#  MIT License.                                                                   #
#                                                                                 #
#  Permission is hereby granted, free of charge, to any person obtaining a copy   #
#  of this software and associated documentation files (the "Software"), to deal  #
#  in the Software without restriction, including without limitation the rights   #
#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #
#  copies of the Software, and to permit persons to whom the Software is          #
#  furnished to do so, subject to the following conditions:                       #
#                                                                                 #
#  The above copyright notice and this permission notice shall be included in all #
#  copies or substantial portions of the Software.                                #
#                                                                                 #
#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #
#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #
#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #
#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #
#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #
#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #
#  SOFTWARE.                                                                      #
#=================================================================================#