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elasticdeform: Elastic deformations for N-dimensional images

Gijs van Tulder


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  <identifier identifierType="DOI">10.5281/zenodo.4569691</identifier>
  <creators>
    <creator>
      <creatorName>Gijs van Tulder</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1635-5423</nameIdentifier>
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  <titles>
    <title>elasticdeform: Elastic deformations for N-dimensional images</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-03-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
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    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4569691</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/gvtulder/elasticdeform/tree/v0.4.9</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4563333</relatedIdentifier>
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  <version>v0.4.9</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This library implements elastic grid-based deformations for N-dimensional images.&lt;/p&gt;

&lt;p&gt;The elastic deformation approach is described in&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Ronneberger, Fischer, and Brox, &amp;quot;U-Net: Convolutional Networks for Biomedical Image Segmentation&amp;quot; (&lt;a href="https://arxiv.org/abs/1505.04597"&gt;https://arxiv.org/abs/1505.04597&lt;/a&gt;)&lt;/li&gt;
	&lt;li&gt;&amp;Ccedil;i&amp;ccedil;ek et al., &amp;quot;3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation&amp;quot; (&lt;a href="https://arxiv.org/abs/1606.06650"&gt;https://arxiv.org/abs/1606.06650&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;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&amp;nbsp;&lt;code&gt;elasticdeform.tf&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;See &lt;a href="https://github.com/gvtulder/elasticdeform"&gt;https://github.com/gvtulder/elasticdeform&lt;/a&gt;&lt;/p&gt;</description>
  </descriptions>
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