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5.1.0

We are happy to announce the [Insight Toolkit (ITK)](https://itk.org) 5.1.0 release! :tada: ITK is an open-source, cross-platform toolkit for N-dimensional scientific image processing, segmentation, and registration.

ITK 5.1.0 is a feature release that improves and extends the major ITK 5.0 release. ITK 5.1.0 includes a [NumPy](https://numpy.org) and [Xarray](https://xarray.pydata.org) filter interface, clang-format enforced coding style, enhanced modern C++ range support, strongly-typed enum's, and much more.

A number of issues were addressed based on feedback from Release Candidate 3. Filters avoid extra copies when operating on NumPy arrays, and `itk.Image` is now a NumPy array-like. Remote module CI testing infrastructure has been migrated to GitHub Actions for C++ tests, Python package builds, and automated Python package deployment.


Downloads
---------

**Python Packages**

Install [ITK Python packages](https://itkpythonpackage.readthedocs.io/en/latest/Quick_start_guide.html) with:


pip install --upgrade itk


or:


conda install -c conda-forge itk


**Guide and Textbook**

- [InsightSoftwareGuide-Book1-5.1.0.pdf](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightSoftwareGuide-Book1-5.1.0.pdf)
- [InsightSoftwareGuide-Book2-5.1.0.pdf](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightSoftwareGuide-Book2-5.1.0.pdf)

**Library Sources**

- [InsightToolkit-5.1.0.tar.gz](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightToolkit-5.1.0.tar.gz)
- [InsightToolkit-5.1.0.zip](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightToolkit-5.1.0.zip)

**Testing Data**

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

- [InsightData-5.1.0.tar.gz](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightData-5.1.0.tar.gz)
- [InsightData-5.1.0.zip](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/InsightData-5.1.0.zip)

**Checksums**

- [MD5SUMS](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/MD5SUMS)
- [SHA512SUMS](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1.0/SHA512SUMS)



Features
--------

![ITKParabolicMorphology](https://imgur.com/MowNzqL.png)


Parabolic morphological filtering with the [ITKParabolicMorphology](https://github.com/InsightSoftwareConsortium/ITKParabolicMorphology) remote module. To install the new Python package: `pip install itk-parabolicmorphology`. Jupyter notebooks are provided as [examples](https://github.com/InsightSoftwareConsortium/ITKParabolicMorphology/tree/master/examples). For more information, see the [Insight Journal article](http://www.insight-journal.org/browse/publication/228), *Beare R. Morphology with parabolic structuring elements. The Insight Journal. January-June. 2008. http://www.insight-journal.org/browse/publication/228*.

GitHub Actions for ITK Remote Module Testing, Packaging, PyPI Deployment

A [GitHub Action configuration](https://gist.github.com/thewtex/aab3f401b27dfd262e508cf1019853f0) is available for ITK Remote Module continuous integration (CI) testing and Python packaging on Linux, macOS, and Windows. Continuous deployment (CD) is configured to upload packages to the [Python Package Index (PyPI)](https://pypi.org) when the repository is tagged. More information can be found in the [ITK Python Package ReadTheDocs](https://itkpythonpackage.readthedocs.io/en/master/Build_ITK_Module_Python_packages.html#github-automated-ci-package-builds) documentation.

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


The [Pythonic, functional-like interface](https://discourse.itk.org/t/itk-5-0-beta-1-pythonic-interface/1271) to all ITK image-to-image-filters now directly supports operation on [NumPy ndarray's](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html), 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)


An `itk.Image` is now more NumPy array-like: `shape`, `ndim`, and `dtype` attributes are available; these correspond to the values when converted to a NumPy `ndarray`. Basic NumPy functions can be called directly on an `itk.Image`, i.e.,


min = np.min(image)
max = np.max(image)
mean = np.mean(image)


Similar, experimental support (subject to change) is also available for [Xarray DataArray's](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html). 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)
print(smoothed)


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]],
dtype=float32)
Coordinates:
* 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
Attributes:
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](https://pythonclock.org), Python 2 support and binaries are no longer 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](https://scikit-image.org/), which uses this pixel type as a default.

clang-format Enforced C++ Coding Style

ITK has adopted a [*.clang-format*](https://github.com/InsightSoftwareConsortium/ITK/blob/master/.clang-format) coding style configuration file so a consistent coding style can automatically be applied to C++ code with the [`clang-format`](http://releases.llvm.org/download.html) 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](https://itk.org/ItkSoftwareGuide.pdf), 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`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ImageBufferRange.html), [`ShapedImageNeighborhoodRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ShapedImageNeighborhoodRange.html), and [`IndexRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1IndexRange.html) classes introduced in ITK 5.0, ITK 5.1 adds an [`ImageRegionRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ImageRegionRange.html). 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)](https://discourse.itk.org/t/itk-5-0-alpha-2-performance/959) 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](https://www.modernescpp.com/index.php/strongly-typed-enums), 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:


<Description>Enum_<EnumValue1>
<Description>Enum_<EnumValue2>
[...]


A guide for updating to the new enum's can be found in the [Strongly Typed Enumerations](https://github.com/InsightSoftwareConsortium/ITK/blob/master/Documentation/ITK5MigrationGuide.md#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

New remote module: [TubeTK](https://github.com/InsightSoftwareConsortium/ITKTubeTK): An open-source toolkit, led by Kitware, Inc., for the segmentation, registration, and analysis of tubes and surfaces in images.

A new [remote module grading system](https://discourse.itk.org/t/remote-module-grading-ranking-system-needed/2750) was added to help convey the quality compliance level for the 45 remote modules.

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*.

Zenodo Citation

ITK has a [Zenodo Citation](https://zenodo.org/badge/latestdoi/800928):

[![DOI](https://zenodo.org/badge/800928.svg)](https://zenodo.org/badge/latestdoi/800928)

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](https://orcid.org/) to our [.zenodo.json](https://github.com/InsightSoftwareConsortium/ITK/blob/master/.zenodo.json) file for authorship association.

NumFOCUS Copyright Transfer

ITK's copyright and the copyright of software held by the [Insight Software Consortium](https://www.insightsoftwareconsortium.org/) have been transferred to [NumFOCUS](https://numfocus.org/). [CMake](https://cmake.org)'s copyright has been transferred to [Kitware](https://kitware.com).

And More

Many more improvements have been made. For details, see the changelogs for the release candidates and changelog below.

Congratulations

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

Of *73 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, Brad T. Moore, Niklas Johansson, Flavien Bridault, Pradeep Garigipati, haaput, tabish, and Antoine Robert, Ben Wilson, Adam Rankin, PA Rodesch, Tabish Syed, vlibertiaux, Michael Jackson, Conrad Poelman, and muschellij2.


**Enjoy ITK!**

5.1rc03

We are happy to announce the [Insight Toolkit (ITK)](https://itk.org) 5.1 Release Candidate 3 is available for testing! :tada: 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.

Release Candidate 3 adds a [remote module rating quality grading system](https://discourse.itk.org/t/remote-module-grading-ranking-system-needed/2750), a new remote module, [TubeTK](https://github.com/InsightSoftwareConsortium/ITKTubeTK), and improvements based on experience with Release Candidate 2.

Downloads
---------

**Python Packages**

Install [ITK pre-release binary Python packages](https://itkpythonpackage.readthedocs.io/en/latest/Quick_start_guide.html) with:


pip install --pre itk


**Library Sources**

- [InsightToolkit-5.1rc03.tar.gz](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/InsightToolkit-5.1rc03.tar.gz)
- [InsightToolkit-5.1rc03.zip](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/InsightToolkit-5.1rc03.zip)

**Testing Data**

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

- [InsightData-5.1rc03.tar.gz](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/InsightData-5.1rc03.tar.gz)
- [InsightData-5.1rc03.zip](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/InsightData-5.1rc03.zip)

**Checksums**

- [MD5SUMS](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/MD5SUMS)
- [SHA512SUMS](https://github.com/InsightSoftwareConsortium/ITK/releases/download/v5.1rc03/SHA512SUMS)



Features
--------

![ITKTubeTK](https://i.imgur.com/sCLSSRD.gif)


Brain vessels segmented with the new [TubeTK](https://github.com/InsightSoftwareConsortium/ITKTubeTK) remote module. To install experimental Python packages: `pip install itk-tubetk`. Jupyter notebooks are provided as [examples](https://github.com/InsightSoftwareConsortium/ITKTubeTK/tree/master/examples).


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


The [Pythonic, functional-like interface](https://discourse.itk.org/t/itk-5-0-beta-1-pythonic-interface/1271) to all ITK image-to-image-filters now directly supports operation on [NumPy ndarray's](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html), 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](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html). 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)
print(smoothed)


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]],
dtype=float32)
Coordinates:
* 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
Attributes:
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](https://pythonclock.org), 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](https://scikit-image.org/), which uses this pixel type as a default.

clang-format Enforced C++ Coding Style

ITK has adopted a [*.clang-format*](https://github.com/InsightSoftwareConsortium/ITK/blob/master/.clang-format) coding style configuration file so a consistent coding style can automatically be applied to C++ code with the [`clang-format`](http://releases.llvm.org/download.html) 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](https://itk.org/ItkSoftwareGuide.pdf), 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`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ImageBufferRange.html), [`ShapedImageNeighborhoodRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ShapedImageNeighborhoodRange.html), and [`IndexRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1IndexRange.html) classes introduced in ITK 5.0, ITK 5.1 adds an [`ImageRegionRange`](https://itk.org/Doxygen/html/classitk_1_1Experimental_1_1ImageRegionRange.html). 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)](https://discourse.itk.org/t/itk-5-0-alpha-2-performance/959) 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](https://www.modernescpp.com/index.php/strongly-typed-enums), 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:


<Description>Enum_<EnumValue1>
<Description>Enum_<EnumValue2>
[...]


A guide for updating to the new enum's can be found in the [Strongly Typed Enumerations](https://github.com/InsightSoftwareConsortium/ITK/blob/master/Documentation/ITK5MigrationGuide.md#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

New remote module: [TubeTK](https://github.com/InsightSoftwareConsortium/ITKTubeTK): An open-source toolkit, led by Kitware, Inc., for the segmentation, registration, and analysis of tubes and surfaces in images.

A new [remote module grading system](https://discourse.itk.org/t/remote-module-grading-ranking-system-needed/2750) was added to help convey the quality compliance level for the 45 remote modules.

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](https://zenodo.org/badge/latestdoi/800928):

[![DOI](https://zenodo.org/badge/800928.svg)](https://zenodo.org/badge/latestdoi/800928)

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](https://orcid.org/) to our [.zenodo.json](https://github.com/InsightSoftwareConsortium/ITK/blob/master/.zenodo.json) file for authorship association.

NumFOCUS Copyright Transfer

ITK's copyright and the copyright of software held by the [Insight Software Consortium](https://www.insightsoftwareconsortium.org/) have been transferred to [NumFOCUS](https://numfocus.org/). [CMake](https://cmake.org)'s copyright has been transferred to [Kitware](https://kitware.com).

And More

Many more improvements have been made. For details, see the changelog below.

Congratulations

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

Of the *26 authors* who contributed since v5.1rc02 and *71 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, Brad T. Moore, Niklas Johansson, Flavien Bridault, Pradeep Garigipati, haaput, tabish, and Antoine Robert.

And the new contributors since v5.1rc02: Ben Wilson, Adam Rankin, PA Rodesch, Tabish Syed, vlibertiaux, and Michael Jackson.


What's Next
-----------


As we work towards the [ITK 5.1.0 release](https://github.com/InsightSoftwareConsortium/ITK/milestone/14), the library will be improved based based on experiences with this final release candidate. Please try out the current release candidate, and discuss your experiences at [discourse.itk.org](https://discourse.itk.org). Contribute with pull requests, code reviews, and issue discussions in our [GitHub Organization](https://github.com/InsightSoftwareConsortium).

**Enjoy ITK!**

5.1rc02

5.1rc01

5.1b01

5.0.1

------------------------------


Bradley Lowekamp (8):
BUG: Fix ProcessObject::RemoveOutput for null objects.
BUG: Use ProcessObject GetInput to obtain base pointer
ENH: Add testing for CastImageFilter for more type conversions
BUG: Restore support for Cast between explicitly cast-ed pixel type
BUG: Install FFTW headers in same location as ITK
COMP: Fix not marked 'override' for ImageSink destructor
BUG: Use enable_if with SFINAE to dispatch
BUG: Specify specific CircleCI docker image with platform

Dženan Zukić (5):
BUG: resample filter no longer triggers unnecessary exception
COMP: forgotten class for ITKV4_COMPATIBILITY in 2aae174
COMP: fix warning about missing override in CastImageFilter
STYLE: Add ITK prefix to testing macros in release branch
ENH: documenting supported compilers

Francois Budin (2):
BUG: ImageBase regions and ImageRegion properties are returned as reference
DOC: Add ITK 5.0 release notes

GenevieveBuckley (1):
BUG: All exceptions must be derived from python's BaseException class

Matthew McCormick (16):
ENH: New content links for ITK 5.0.0
STYLE: DeformableRegistration2 line length warnings
COMP: SpatialObjectsHierarchy Software Guide newline
BUG: Add test/CMakeLists.txt stub to NumPy bridge
COMP: Provide NumericTraits<complex<T>>::ZeroValue() definition
BUG: Do not require PyBUF_WRITABLE in GetArrayViewFromImage
DOC: Update supported Python versions warning
BUG: Add PEP 366 __package__ support to ITKLazyModule
BUG: Release the Python Global Interpreter Lock (GIL) during execution
STYLE: Apply ITK Style Guidelines to itkPyCommand.cxx
BUG: itk::PyCommand ensures the GIL state
BUG: Support pickling LazyITKModule with cloudpickle
BUG: Add Pipeline name to Azure configuration
DOC: Update Azure Pipelines badge URL's
BUG: Add wrapping for TransformMeshFilter
ENH: Bump itkVersion.cmake for 5.0.1

Pablo Hernandez-Cerdan (1):
BUG: Fix COMPILE_DEFINITIONS of castxml

Stephen Aylward (4):
ENH: Updated Spatial Object tex to match ITKv5
BUG: Fixed spelling mistakes and use of plural member functions
BUG: Fix grammar and naming mistakes in SpatialObject documentation
DOC: Fixed expected output of examples and documentation

yjcchen0913 (1):
BUG: Ensure strict weak ordering in HessianToObjectnessMeasure's sort

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