Software Open Access
elasticdeform: Elastic deformations for N-dimensional images
Gijs van Tulder
This library implements elastic grid-based deformations for N-dimensional images.
The elastic deformation approach is described in
- Ronneberger, Fischer, and Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation" (https://arxiv.org/abs/1505.04597)
- Çiçek et al., "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation" (https://arxiv.org/abs/1606.06650)
The procedure generates a coarse displacement grid with a random displacement for each grid point. This grid is then interpolated to compute a displacement for each pixel in the input image. The input image is then deformed using the displacement vectors and a spline interpolation.
In addition to the normal, forward deformation, this package also provides a function that can backpropagate the gradient through the deformation. This makes it possible to use the deformation as a layer in a convolutional neural network. For convenience, a TensorFlow wrapper is provided in elasticdeform.tf.
| Name | Size | |
|---|---|---|
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gvtulder/elasticdeform-v0.5.0.zip
md5:2e8bb265c0c782e1e45501c57e3c694d |
126.5 kB | Download |
| All versions | This version | |
|---|---|---|
| Views | 365 | 71 |
| Downloads | 8 | 1 |
| Data volume | 1.0 MB | 126.5 kB |
| Unique views | 292 | 62 |
| Unique downloads | 7 | 1 |
Indexed in
- Publication date:
- September 21, 2022
- DOI:
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- Related identifiers:
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- License (for files):
- Other (Open)