niftynet.layer.rand_elastic_deform module¶
Data augmentation using elastic deformations as used by: Milletari,F., Navab, N., & Ahmadi, S. A. (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation
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class
RandomElasticDeformationLayer
(num_controlpoints=4, std_deformation_sigma=15, proportion_to_augment=0.5, spatial_rank=3)[source]¶ Bases:
niftynet.layer.base_layer.RandomisedLayer
generate randomised elastic deformations along each dim for data augmentation
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__init__
(num_controlpoints=4, std_deformation_sigma=15, proportion_to_augment=0.5, spatial_rank=3)[source]¶ This layer elastically deforms the inputs, for data-augmentation purposes.
Parameters: - num_controlpoints –
- std_deformation_sigma –
- proportion_to_augment – what fraction of the images to do augmentation on
- name – name for tensorflow graph
(may be computationally expensive).
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