ITK 5.1 Release Candidate 2 available for download

March 9, 2020

We are happy to announce the Insight Toolkit (ITK) 5.1 Release Candidate 2 is available for testing! ITK is an open-source, cross-platform toolkit for N-dimensional scientific image processing, segmentation, and registration.

ITK 5.1 is a feature release that improves and extends the major ITK 5.0 release. ITK 5.1 includes a NumPy and Xarray filter interface, clang-format enforced coding style, enhanced modern C++ range support, strongly-typed enum’s, and much more.


Python Packages

Install ITK pre-release binary Python packages with:

pip install --pre itk

Library Sources

Testing Data

Unpack optional testing data in the same directory where the Library Source is unpacked.



ITK Xarray

An itk.Image converted to an xarray.DataArray. Xarray enables processing while preserving metadata, distributed computing with Dask, and machine learning with the scikit-learn API.

Pass NumPy Array’s or Xarray DataArray’s to ITK Image Filters

The Pythonic, functional-like interface to all ITK image-to-image-filters now directly supports operation on NumPy ndarray’s, i.e. numpy.ndarray. If a ndarray is passed as an input, a ndarray is returned as an output.

For example,

smoothed = itk.median_image_filter(array, radius=2)

Previously, explicit conversion to / from an itk.Image was required with itk.array_from_image and itk.image_from_array.

We can now also convert an itk.Image to a numpy.ndarray with the standard np.asarray call.

import numpy as np
import itk

image = itk.imread('/path/to/image.tif')
array = np.asarray(image)

Similar, experimental support (subject to change) is also available for Xarray DataArray’s. If an xarray.DataArray is passed as an input, an xarray.DataArray is returned as an output. Moreover, the operation preserves spatial and dimensional metadata. For example,

import xarray as xr
import itk

image = itk.imread('/path/to/image.tif')
da = itk.xarray_from_image(image)
smoothed = itk.median_image_filter(da, radius=3)

results in:

<xarray.DataArray (y: 288, x: 894)>
array([[255.    , 255.    , 255.    , ..., 255.    , 255.    , 255.    ],
       [ 11.9995,  11.9995,  11.9995, ...,  11.9995,  11.9995,  11.9995],
       [ 11.9995,  11.9995,  11.9995, ...,  11.9995,  11.9995,  11.9995],
       [ 11.9995,  11.9995,  11.9995, ...,  11.9995,  11.9995,  11.9995],
       [ 11.9995,  11.9995,  11.9995, ...,  11.9995,  11.9995,  11.9995],
       [ 11.9995,  11.9995,  11.9995, ...,  11.9995,  11.9995,  11.9995]],
  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 ... 889.0 890.0 891.0 892.0 893.0
  * y        (y) float64 0.0 1.0 2.0 3.0 4.0 ... 283.0 284.0 285.0 286.0 287.0
    direction:  [[1. 0.]\n [0. 1.]]

A round trip is possible with itk.image_from_xarray.

Python 3 Only

ITK 5.1 will be the first Python 3-only release. Consistent with most scientific Python packages and CPython’s 2020 drop in support, Python 2 support and binaries are no longer be available.

Python Package 64-bit Float Support

In addition to the many other pixel types supported, the itk binary Python packages now include support for the double pixel type, i.e. 64-bit IEEE floating-point pixels. This improves compatibility with scikit-image, which uses this pixel type as a default.

clang-format Enforced C++ Coding Style

ITK has adopted a .clang-format coding style configuration file so a consistent coding style can automatically be applied to C++ code with the clang-format binary. A consistent coding style is critical for readability and collaborative development.

clang-format has been applied to the entire codebase. The Whitesmiths style of brace indentation, previously part of the ITK Coding Style Guidelines, is not supported by clang-format, so it has been replaced by a brace style consistent with VTK’s current style.

A Git commit hook will automatically apply clang-format to changed C++ code.

Enhanced Modern C++ Range Support

In addition to the ImageBufferRange, ShapedImageNeighborhoodRange, and IndexRange classes introduced in ITK 5.0, ITK 5.1 adds an ImageRegionRange. These range classes conform to the Standard C++ Iterator requirements so they can be used in range-based for loop’s and passed to Standard C++ algorithms. Range-based for loops provide an elegant syntax for iteration. Moreover, they are often more performant than other iteration classes available.

For example, to add 42 to every pixel:

ImageBufferRange<ImageType> range{ *image };

for (auto&& pixel : range)
  pixel = pixel + 42;

In ITK 5.1, adoption of the range classes was extended across the toolkit, which demonstrates their use and improves toolkit performance.

Point Set Registration Parallelism

ITK provides a powerful registration framework for point-set registration, offering information-theoretic similarity metrics, labeled point-set metrics, and spatial transformation models that range from affine to b-spline to dense displacement fields. ITK 5.1 features enhanced parallelism in point-set metric computation, leveraging the native thread-pool and Threading Building Blocks (TBB) enhancements in ITK 5.

SpatialObject’s and Strongly-Typed enum’s

Improvements and refinements were made to the ITK 5 itk::SpatialObject refactoring, and modern C++ interface. In particular, ITK 5.1 transitions enumerations to strongly-typed enumerations, which is flagged by modern compilers due to improved scoping and implicit conversions to int. Enum names now follow a consistent <Description>Enum naming conversion, which results in a Python interface:


A guide for updating to the new enum’s can be found in the Strongly Typed Enumerations section of the ITK 5 Migration Guide.

DICOM Support

ITK’s broadly adopted medical image support is hardened thanks to 20 years of testing and support from major open source DICOM library maintainers. In this release, many members of the community collaborated to further enhance ITK’s DICOM support for corner cases related to modality, pixel types, and vendor variations.

Remote Module Updates

Many remote modules were updated: AnalyzeObjectMapIO, AnisotropicDiffusionLBR, BSplineGradient, BioCell, BoneEnhancement, BoneMorphometry, Cuberille, FixedPointInverseDisplacementField, GenericLabelInterpolator, HigherOrderAccurateGradient, IOMeshSTL, IOOpenSlide, IOScanco, IOTransformDCMTK, IsotropicWavelets, LabelErodeDilate, LesionSizingToolkit, MinimalPathExtraction, Montage, MorphologicalContourInterpolation, ParabolicMorphology, PhaseSymmetry, RLEImage, RTK, SCIFIO, SimpleITKFilters, SkullStrip, SplitComponents, Strain, SubdivisionQuadEdgeMeshFilter, TextureFeatures, Thickness3D, TotalVariation, and TwoProjectionRegistration. Their updates are included in the detailed changelog below.

Zenodo Citation

ITK has a Zenodo Citation:


This citation can be used to cite a specific version of the software. If you have contributed 10 or more patches to ITK, please add your ORCID iD to our .zenodo.json file for authorship association.

NumFOCUS Copyright Transfer

ITK’s copyright and the copyright of software held by the Insight Software Consortium have been transferred to NumFOCUS. CMake‘s copyright has been transferred to Kitware.

And More

Many more improvements have been made. For details, see the changelog in the full release announcement.


Congratulations and thank you to everyone who contributed to this release.

Of the 30 authors who contributed since v5.1rc01 and 65 authors since v5.0.0, we would like to specially recognize the new contributors:

Mathew J. Seng, Zahil Shanis, yjcchen0913, PA Rodesch, Aurélien Coussat, yinkaola, Bryce Besler, Pierre Chatelier, Rinat Mukhometzianov, Ramraj Chandradevan, Hina Shah, Gordian Kabelitz, Genevieve Buckley, Aaron Bray, nslay, Antoine Robert, James Butler, Matthew Rocklin, Gina Helfrich, and Neslisah Torosdagli.

And the new contributors since v5.1rc01: Brad T. Moore, Niklas Johansson, Flavien Bridault, Pradeep Garigipati, haaput, tabish, and Antoine Robert.

What’s Next

As we work towards the next release candidate, the new strongly typed enum’s will undergo testing. ITK remote modules will update their copyright attribution to NumFOCUS. Please try out the current release candidate, and discuss your experiences at Contribute with pull requests, code reviews, and issue discussions in our GitHub Organization.

Enjoy ITK!


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