March 1, 2021 Software Open Access

Gijs van Tulder

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{
  "description": "<p>This library implements elastic grid-based deformations for N-dimensional images.</p>\n\n<p>The elastic deformation approach is described in</p>\n\n<ul>\n\t<li>Ronneberger, Fischer, and Brox, &quot;U-Net: Convolutional Networks for Biomedical Image Segmentation&quot; (<a href=\"https://arxiv.org/abs/1505.04597\">https://arxiv.org/abs/1505.04597</a>)</li>\n\t<li>&Ccedil;i&ccedil;ek et al., &quot;3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation&quot; (<a href=\"https://arxiv.org/abs/1606.06650\">https://arxiv.org/abs/1606.06650</a>)</li>\n</ul>\n\n<p>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.</p>\n\n<p>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&nbsp;<code>elasticdeform.tf</code>.</p>\n\n<p>See <a href=\"https://github.com/gvtulder/elasticdeform\">https://github.com/gvtulder/elasticdeform</a></p>", 
  "license": "", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0003-1635-5423", 
      "@type": "Person", 
      "name": "Gijs van Tulder"
    }
  ], 
  "url": "https://zenodo.org/record/4569691", 
  "codeRepository": "https://github.com/gvtulder/elasticdeform/tree/v0.4.9", 
  "datePublished": "2021-03-01", 
  "version": "v0.4.9", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.4569691", 
  "@id": "https://doi.org/10.5281/zenodo.4569691", 
  "@type": "SoftwareSourceCode", 
  "name": "elasticdeform: Elastic deformations for N-dimensional images"
}