SimpleITK

<h2 id="history">History</h2>
<p>The initial development of SimpleITK was funded by the <a href="/facts/United_States_National_Library_of_Medicine/Hzty0MCX">United States National Library of Medicine</a> under the <a href="/facts/American_Recovery_and_Reinvestment_Act_of_2009/FFeMjKD0">American Recovery and Reinvestment Act (ARRA)</a> program as a collaboration between The Mayo Clinic, Kitware Inc, The University of Iowa and NLM's intramural program. The first major release of the toolkit was <a href="https://www.nlm.nih.gov/news/nlm_releases_simpleitk.html">announced in April-May 2017</a>. The second major release was announced in September 2020.
</p><p>Between 2013 and 2019, SimpleITK development was primarily carried out as part of the intramural research program of the National Library of Medicine with collaborators at The University of Iowa and Monash University. Since 2019, SimpleITK development is primarily carried out under the Office of Cyber Infrastructure and Computational Biology at the <a href="/facts/National_Institute_of_Allergy_and_Infectious_Diseases/PD8bQnvS">National Institute of Allergy and Infectious Diseases</a>. In April 2020 the toolkit changed its logo to a more modern design.
</p>
<h2 id="examples">Examples</h2>
<h3>Gaussian smoothing</h3>
<p>Short Python scripts illustrating image reading, blurring, and writing. Using the object oriented interface:
</p>
import SimpleITK as sitk
import sys

if len(sys.argv) < 4:
    print("Usage: SimpleGaussian <input> <sigma> <output>")
    sys.exit(1)

reader = sitk.ImageFileReader()
reader.SetFileName(sys.argv[1])
image = reader.Execute()

pixelID = image.GetPixelID()

gaussian = sitk.SmoothingRecursiveGaussianImageFilter()
gaussian.SetSigma(float(sys.argv[2]))
image = gaussian.Execute(image)

caster = sitk.CastImageFilter()
caster.SetOutputPixelType(pixelID)
image = caster.Execute(image)

writer = sitk.ImageFileWriter()
writer.SetFileName(sys.argv[3])
writer.Execute(image)

<p>A more concise version using the procedural interface:
</p>
import SimpleITK as sitk
import sys

if len(sys.argv) < 4:
    print("Usage: SimpleGaussian <input> <sigma> <output>")
    sys.exit(1)

image = sitk.ReadImage(sys.argv[1])
pixelID = image.GetPixelID()
image = sitk.Cast(sitk.SmoothingRecursiveGaussian(image, float(sys.argv[2])), pixelID)
sitk.WriteImage(image, sys.argv[3])

<h3>Multi-modality Rigid Registration</h3>
<p>Short R script illustrating the use of the library's registration framework for rigid registration
of two 3D images:
</p>
library(SimpleITK)

args = commandArgs( trailingOnly=TRUE )
if( length( args ) < 2 )
{
	cat( "Usage: registration <fixed_image> <moving_image> <output_transform>\n" )
	quit( save="no", status=1 )
}
fixed_image <- ReadImage( args[1], "sitkFloat32" )
moving_image <- ReadImage( args[2], "sitkFloat32" )

initial_transform <- CenteredTransformInitializer( fixed_image,
                                                   moving_image,
                                                   Euler3DTransform(),
                                                   "GEOMETRY" )
reg <- ImageRegistrationMethod()
reg$SetMetricAsMattesMutualInformation( numberOfHistogramBins=50 )
reg$SetMetricSamplingStrategy( "RANDOM" )
reg$SetMetricSamplingPercentage( 0.01 )
reg$SetInterpolator( "sitkLinear" )
reg$SetOptimizerAsGradientDescent( learningRate=1.0,
numberOfIterations=100 )
reg$SetOptimizerScalesFromPhysicalShift()
reg$SetInitialTransform( initial_transform, inPlace=FALSE )
final_transform <- reg$Execute( fixed_image, moving_image )

WriteTransform( final_transform, "final_transform.tfm" )

<h2 id="external-links">External links</h2>
<ul><li><a href="http://www.simpleitk.org/">Official website</a>.</li>
<li>SimpleITK: A Simplified Path to Insight - <a href="https://simpleitk.org/TUTORIAL/">an online tutorial</a> using Jupyter notebooks in Python.</li>
<li><a href="https://github.com/SimpleITK">Organization on GitHub</a></li>
<li>Short examples illustrating how to use some of the library components are available on <a href="https://simpleitk.readthedocs.io/en/master/link_examples.html">read the docs</a>.</li>
<li><a href="https://itk.org/SimpleITKDoxygen/html/">Class and procedure documentation</a> is available via <a href="/facts/Doxygen/Uw1w2lNh">Doxygen</a>.</li>
<li><a href="https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks">Jupyter notebooks on GitHub</a> with long and extensively documented examples, useful for learning and teaching how to work with SimpleITK.</li>
<li>Get help, post questions on the <a href="https://discourse.itk.org/">ITK discussion forum</a>.</li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>R. Beare, B. C. Lowekamp, Z. Yaniv, “Image Segmentation, Registration and Characterization in R with SimpleITK”, J Stat Softw, 86(8), 2018, doi:10.18637/jss.v086.i08. <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>B. C. Lowekamp, D. T. Chen, L. Ibáñez, D. Blezek, "The Design of SimpleITK", Front. Neuroinform.,7:45, 2013, doi:10.3389/fninf.2013.00045. <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
<li id="fn:3"><p>Z. Yaniv, B. C. Lowekamp, H.J. Johnson, R. Beare, "SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research", J Digit Imaging.,  31(3):290-303, 2018, doi: 10.1007/s10278-017-0037-8. <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li>
<li id="fn:4"><p>M. D. Blackledge, D. J.Collins, D-M Koh, M. O. Leach, "Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX", Comput Biol Med., 69:203-212, 2016, doi: 10.1016/j.compbiomed.2015.12.002 <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li>
<li id="fn:5"><p>J. J. M. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. H. Beets-Tan, J. C. Fillon-Robin, S. Pieper, H. J. W. L. Aerts, "Computational Radiomics System to Decode the Radiographic Phenotype", Cancer Research, 77(21): e104–e107, 2017, doi: 10.1158/0008-5472.CAN-17-0339 <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li>
<li id="fn:6"><p>K. Marstal, F. Berendsen, M. Staring, S. Klein, "SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration",  IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 574-582, 2016, doi:10.1109/CVPRW.2016.78 <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li>
<li id="fn:7"><p>E. Gibson, W. Li, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso, T. Vercauteren, "NiftyNet: a deep-learning platform for medical imaging", Comput Methods Programs Biomed., 158:113-122, 2018, doi: 10.1016/j.cmpb.2018.01.025 <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li>
</ol>

SimpleITK open-in-new

SimpleITK