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elasticdeform: Elastic deformations for N-dimensional images

Gijs van Tulder


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    <subfield code="a">&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;</subfield>
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