SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.
This is an automatic pre-release packaging of SimpleITK based on the master branch. It contains the latest features and experimental developments.
To upgrade to the latest pre-release Python binary package run:
pip install --upgrade --pre SimpleITK --find-links https://github.com/SimpleITK/SimpleITK/releases/tag/latest
This list of changes was auto generated.
Announcing the SimpleITK 2.3.1 Release!
The release includes fixes to behavior regression, compilation issues, and support for Python 3.12.
42ce27df Bump patch version to 2.3.1 da780800 Explicitly install setuptools in environment ad198eb3 Add Python 3.12 packaging support 0ca94ef6 Address missing Generic Label Interpolator 27112d21 Fix undeleted N4 filter in example 0b0a492e Use reusable MockLogger to capture warning messages 72ee8bf3 Restore and depricate MaskImageFilter support for mask input types
Announcing the SimpleITK 2.3.0 Release!
The release includes new features, behavior changes, documentation updates and bug fixes.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
Python binary wheels are available for download from PyPI. It is important to have the latest version of pip for correct wheel compatibility and installation. To install the latest SimpleITK package:
python -m pip install --upgrade pip
python -m pip install SimpleITK
The packages are available on conda-forge with dependecies on the ecosystem:
conda install --channel conda-forge simpleitk
Starting with SimpleITK 2.3.0, binary packages will no longer be available in the "simpleitk" anaconda channel. Older versions continue to be available in that channel.
Thank you to all contributors to SimpleITK and ITK. The new contributors to SimpleITK include @mbopfNIH @kaspermarstal @umasehs @caolonghao @wbzyl.
sitkLabelLinear interpolator for multi-label images. The implementation is the LabelImageGenericInterpolateImageFunction class from GenericLabelInterpolator ITK remote module. ( Contributed by @dyollb )__setitem__ as index parameter. The mask is considered a binary mask where assignment occurs. For example img[img<0] = 0 can be used to remove negative numbers.MinimumMaximum() -> Tuple[float, float] procedure for MinimumMaximumImageFilter.pathlib.Path support to Transform IO methods.Clamp boolean option to UnsharpMaskFilter.Image::ToVector and Image::ToScalar methods to perform fast in-place conversion between vector pixel types (VectorImage), and high spatial dimension scalar images.FastMarchingBaseImageFilter and FastMarchingImageFilter.KernelType parameter RankImageFilter to support non box kernel shapes.MaskedAssignImageFilter.AssignConstant to MaskedAssignImageFilter and support for vector input images.NPasteImageFilter with PasteImageFilter.N4BiasFieldCorrectionImageFilter measurements of CurrentLevel, ElapsedIterations, and CurrentConvergenceMeasurement for observers.FFTNormalizedCorrelationImageFilter to use named inputs.RequiredFractionOfOverlappingPixels parameter to FFTNormalizedCorrelationImageFilter.MaskImageFilter and MaskNegatedImageFilter to only support sitkUInt8 pixel types for masked input. This unifies the supported mask image types between these two filters and the MaskedAssignedImageFilter to match SimpleITK's common conventions for mask images.
MaskImageFilter it is recommended to update to casting the mask input parameter to sitkUInt8 pixel type. This is compatible with versions before SimpleITK 2.0.MaskNegatedImageFilter old behavior erroneously expected both inputs to be of the same type. The new behavior expects the inputs to be of sitkUInt8
-flatstaticmethod argument to SWIG. The SimpleITK_PYTHON_FLATSTATICMETHOD CMake variable has been added to control the usage of this flag. It is currently enabled by default and is planned to default to OFF in future releases.GTest::Main issue with using ITK out of a build tree.FastMarchingUpwindGradientImageFilter setting target point before setting stopping criteria.LandmarkBasedInitialization for Similarity3DTransforms.LaplacianSharpening to update for ITK v5.4 changes.Announcing the SimpleITK 2.3 Release Candidate 2!
This is expected to be the final RC before the 2.3.0 release. Users and developers are encouraged to test the RC before the final release and report issues, bugs, and any compatibility problems.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
python -m pip install --upgrade pip
python -m pip install --pre SimpleITK --find-links https://github.com/SimpleITK/SimpleITK/releases/tag/v2.3rc2
Removed Python 3.7 packaging.
Restore functions for static members like ImageSeriesReader_GetGDCMSeriesIDs (restores 2.2 behavior).
SWIG 4.1.0 change behavior to removing flattened static methods for objects. The compatible behavior can be restored by adding -flatstaticmethod argument to SWIG. The SimpleITK_PYTHON_FLATSTATICMETHOD CMake variable has been added to control the usage of this flag. It is currently enabled by default and is planned to default to OFF in future releases.
LaplacianSharpening to update for ITK v5.4 changes.Announcing the SimpleITK 2.3 Release Candidate 1!
Users and developers are encouraged to test the RC before the final release and report issues, bugs, and any compatibility problems.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
python -m pip install --upgrade pip
python -m pip install --pre SimpleITK --find-links https://github.com/SimpleITK/SimpleITK/releases/tag/v2.3rc1
In Python, add support for masked images to __setitem__ as index parameter. The mask is considered a binary mask where assignment occurs. For example img[img<0] = 0 can be used to remove negative numbers.
In Python, add MinimumMaximum() -> Tuple[float, float] procedure for MinimumMaximumImageFilter.
In Python, add pathlib.Path support to Transform IO methods.
Add Clamp boolean option to UnsharpMaskFilter.
Add Image::ToVector and Image::ToScalar methods to perform fast in-place conversion between vector pixel types (VectorImage), and high spatial dimension scalar images.
Add initial seed values to FastMarchingBaseImageFilter and FastMarchingImageFilter.
Add KernelType parameter RankImageFilter to support non box kernel shapes.
Wrap MaskedAssignImageFilter.
Add AssignConstant to MaskedAssignImageFilter and support for vector input images.
Internally replace NPasteImageFilter with PasteImageFilter.
Add to N4BiasFieldCorrectionImageFilter measurements of CurrentLevel, ElapsedIterations, and CurrentConvergenceMeasurement for observers.
Update FFTNormalizedCorrelationImageFilter to use named inputs.
Add missing RequiredFractionOfOverlappingPixels parameter to FFTNormalizedCorrelationImageFilter.
Various improvements to SimpleElastix integration and support.
Change MaskImageFilter and MaskNegatedImageFilter to only support sitkUInt8` pixel types for masked input. Previously, the filter erroneously expected both inputs to be of the same type.
Fix already defined GTest::Main issue with using ITK out of a build tree.
Add an internal "Proxy" image to safely support exceptions with certain inplace operations in C++ and Python.
Fix FastMarchingUpwindGradientImageFilter setting target point before setting stopping criteria.
Test support of LandmarkBasedInitialization for Similarity3DTransforms.
Added to Sphinx generated documentation images and text output such as in Fast Marching Segmentation and Image Registration Method1 examples. Fixed bug in example, working with DICOM tags. Update JSON docs form ITK XML Document specifying pixel type as part of reading. Add information on dependency between GetGDCMSeriesIDs and GetGDCMSeriesFileNames. In DicomTagsExample, included example run of DicomImagePrintTags code in the Sphinx docs. Fix time date ordering in string in DicomSeriesFromArray.R example.
Require C++17 for building SimpleITK. Update GTest Superbuild version to 1.13.0 Update Swig Superbuild version to 4.1.1 Various C++17 modernization in testing include more usage of initializer lists, and namespaces. Change PimpleImageBase to return unique_ptr over raw. Address unused return value in image transform point methods and in transform tests. Use unique_ptr for Transform interface. Remove references to legacy SITK_4D_IMAGES definition. Enable ITK_LEGACY_REMOVE by default in Superbuild. Update ITK enum to remove legacy enums type. Update to CSharp DotNet version 4 by default. Update SimpleITK Superbuild Lua to 5.4.4 and enable usage of Lua 5.4. Remove disutils and setupegg.py from Python packaging fallback. Add pyproject.toml to address wheel dependency warning
SimpleITK 2.2.1 has been released!
The patch release includes bug fixes, and updates.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
Python binary wheels are available for download from PyPI. It is important to have the latest version of pip for correct wheel compatibility and installation. To install the latest SimpleITK package:
python -m pip install --upgrade pip
python -m pip install SimpleITK
SimpleITK packages are available for the conda Python package manager as a monolithic package:
conda install -c simpleitk simpleitk
The packages are also on conda-forge with dependecies on the ecosystem:
conda install --channel conda-forge simpleitk
Update ITK to 5.3.0 tagged release. Add Python 3.11 binary packages. Update LabelOverlapMeasures with ITK 5.3 changes: existing computation for FalsePositiveError was renamed to FalseDiscoveryRate, and the computation for FPE corrected.
Fix segmentation fault with exception in inplace operators, caused by invalid images after C++ move. Fix viewer test to use system python executable. Fix duplicate GTest CMake configuration from ITK Support CMake DOWNLOAD_EXTRACT_TIMESTAMP option for correct SWIG and PCRE file timestamps. Fix numpy character dtype conversion warning. Fix error with itk::LabelOverlapMeasuresImageFilter::SetInput.
Update Github actions to fix warning. Fix CircleCI Python 3.8 builds.
SimpleITK 2.2.0 has been released!
The release includes new features, API changes, documentation updates and bug fixes.
A highlight of this release is the addition of SimpleElastix as a compile time option in SimpleITK.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
Python binary wheels are available for download from PyPI. It is important to have the latest version of pip for correct wheel compatibility and installation. To install the latest SimpleITK package:
python -m pip install --upgrade pip
python -m pip install SimpleITK
Included in this release are "manylinux2014_aarch64" wheels to provide support for the ARMv8-A (aarch64) on Linux.
SimpleITK packages are available for the conda Python package manager as a monolithic package:
conda install -c simpleitk simpleitk
Announcing the SimpleITK 2.2 Release Candidate 4!
Users and developers are encouraged to test the RC before the final release and report issues, bugs, and any compatibility problems. This is the final RC before the 2.2 final release.
In collaboration with the Elastix team, the addition of SimpleElastix as a compile time option is the highlight of this release. Please download the SimpleITK source and compile with the CMake SimpleITK_USE_ELASTIX option enabled to try out this experimental feature.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
python -m pip install --upgrade pip
python -m pip install --pre SimpleITK --find-links https://github.com/SimpleITK/SimpleITK/releases/tag/v2.2rc4
Announcing the SimpleITK 2.2 Release Candidate 3!
Users and developers are encouraged to test the RC before the final release and report issues, bugs, and any compatibility problems. The SimpleITK 2.2.0 final is planned to be published shortly after the next ITK tag, either v5.3rc04 or v5.3.0.
Complete instructions on getting started with SimpleITK including downloading binaries or building SimpleITK can be found on the SimpleITK Read the Docs web page.
python -m pip install --upgrade pip
python -m pip install --pre SimpleITK --find-links https://github.com/SimpleITK/SimpleITK/releases/tag/v2.2rc3
Conda packages are available from Anaconda Cloud on the SimpleITK channel. These can be installed with:
conda install -c simpleitk/label/dev simpleitk
Note that pre-release packages with the "dev" channel may be deleted in the future after the releases are made.
This release was yanked from PyPI, due to a partial upload cause by an account limitation.
Updates to build infrastructure and including Python 3.10 binaries.