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 | |
|---|---|---|
|
gvtulder/elasticdeform-v0.4.8.zip
md5:7ccdb702b4f67420727b3f340d51603f |
125.3 kB | Download |
| All versions | This version | |
|---|---|---|
| Views | 187 | 31 |
| Downloads | 7 | 1 |
| Data volume | 877.6 kB | 125.3 kB |
| Unique views | 137 | 19 |
| Unique downloads | 6 | 1 |
Indexed in
- Publication date:
- February 26, 2021
- DOI:
-
- Related identifiers:
- Supplement to
- License (for files):
- Other (Open)