itk-numerics
ITK is an open-source toolkit for multidimensional image analysis
Decision gist · record as of 2026-08-14
Yes, if you need ITK's image processing and segmentation capabilities in Python. The package is actively maintained, permissively licensed, has no known vulnerabilities, and offers pre-built wheels for easy installation. Medium install friction is typical for compiled numerical libraries. Verify whether itk-numerics provides the specific algorithms you need versus installing the broader itk package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires itk-core as a runtime dependency; pre-built wheels available for Python 3.8+ on Linux, macOS, and Windows.
- Medium install friction due to compiled binary wheels.
- Released 7 days ago with active maintenance (last commit 2026-08-14).
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits both commercial and non-commercial use with permissive terms. Requires attribution and notice of modifications, but imposes no copyleft obligations on derivative works.
last release 2026-08-07 (7 days) · last repo commit 2026-08-14 · 1,643 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 233,090 downloads/mo, #9,048 on PyPI
Alternatives
Verify before relying
pip install itk-numerics
import itk
# Use ITK's image processing functions via itk-core dependency- Specific numerical algorithms and API surface provided by itk-numerics versus the broader itk package
- Performance characteristics or computational limits for large image datasets
- Whether itk-numerics is a subset or specialized distribution of ITK's full toolkit
What it is and what it does
itk-numerics is a Python package that exposes ITK's core numerical image processing capabilities. ITK (Insight Toolkit) is an open-source, cross-platform toolkit designed for N-dimensional scientific image processing, segmentation, and registration—tasks common in medical imaging and research. The package depends on itk-core and provides pre-built binary wheels for rapid installation across major platforms and architectures.
Typically used in medical image analysis workflows where you need to segment anatomical structures from CT or MRI scans, align multiple imaging modalities, or apply numerical transformations to volumetric data. The toolkit is actively maintained by the Insight Software Consortium under NumFOCUS sponsorship, with a long history of use in research and clinical applications.
Use it for
- Segment organs or lesions from medical imaging data (CT, MRI) for diagnostic or surgical planning
- Register and align multiple medical images to combine information from different modalities or time points
- Apply N-dimensional filtering, morphological operations, and transformations to scientific image data
- Build reproducible image analysis pipelines for research or clinical decision support systems
- Integrate ITK's numerical algorithms into Python-based medical imaging workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need ITK's image processing and segmentation capabilities in Python.
The package is actively maintained, permissively licensed, has no known vulnerabilities, and offers pre-built wheels for easy installation. Medium install friction is typical for compiled numerical libraries. Verify whether itk-numerics provides the specific algorithms you need versus installing the broader itk package.
Install
itk-numerics on PyPI
Before you install
Medium install friction due to compiled binary wheels. Released 7 days ago with active maintenance (last commit 2026-08-14). Supports Python 3.8+ across Linux, macOS (including Apple Silicon), and Windows with pre-built wheels for common architectures.
Requires itk-core as a runtime dependency; pre-built wheels available for Python 3.8+ on Linux, macOS, and Windows.
License in practice
Apache License 2.0 permits both commercial and non-commercial use with permissive terms. Requires attribution and notice of modifications, but imposes no copyleft obligations on derivative works.
Quickstart
pip install itk-numerics
import itk
# Use ITK's image processing functions via itk-core dependencyVerify before relying
- Specific numerical algorithms and API surface provided by itk-numerics versus the broader itk package
- Performance characteristics or computational limits for large image datasets
- Whether itk-numerics is a subset or specialized distribution of ITK's full toolkit
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packageitk-core |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 233,090 / month, #9,048 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: AndroidOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: C++Programming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Medical Science Apps.Topic :: Software Development :: Libraries |
Evidence: itk_numerics-5.4.7-cp310-cp310-macosx_10_9_x86_64.whl; itk_numerics-5.4.7-cp310-cp310-macosx_11_0_arm64.whl; itk_numerics-5.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_numerics-5.4.7-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_numerics-5.4.7-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_numerics-5.4.7-cp310-cp310-win_amd64.whl; itk_numerics-5.4.7-cp311-abi3-macosx_10_9_x86_64.whl; itk_numerics-5.4.7-cp311-abi3-macosx_11_0_arm64.whl; itk_numerics-5.4.7-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_numerics-5.4.7-cp311-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_numerics-5.4.7-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_numerics-5.4.7-cp311-abi3-win_amd64.whl; itk_numerics-5.4.7-cp39-cp39-macosx_10_9_x86_64.whl; itk_numerics-5.4.7-cp39-cp39-macosx_11_0_arm64.whl; itk_numerics-5.4.7-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; itk_numerics-5.4.7-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; itk_numerics-5.4.7-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; itk_numerics-5.4.7-cp39-cp39-win_amd64.whl
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See also itk-core · itk-segmentation · itkwasm · itk-filtering · simpleitk · itk · itk-registration · itk-io · vtk · TotalSegmentator