March 1, 2021 Software Open Access

Gijs van Tulder

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  <dc:creator>Gijs van Tulder</dc:creator>
  <dc:date>2021-03-01</dc:date>
  <dc:description>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.

See https://github.com/gvtulder/elasticdeform</dc:description>
  <dc:identifier>https://zenodo.org/record/4569691</dc:identifier>
  <dc:identifier>10.5281/zenodo.4569691</dc:identifier>
  <dc:identifier>oai:zenodo.org:4569691</dc:identifier>
  <dc:relation>url:https://github.com/gvtulder/elasticdeform/tree/v0.4.9</dc:relation>
  <dc:relation>doi:10.5281/zenodo.4563333</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:title>elasticdeform: Elastic deformations for N-dimensional images</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
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