ITK is an open-source toolkit for multidimensional image analysis

These details have been verified by PyPI

Maintainers

These details have not been verified by PyPI

Project links

Project description

ITK - The Insight Toolkit

ITK: The Insight Toolkit

	C++	Python
Linux
Windows
macOS
macOS (Apple Silicon)
Linux (Code coverage)

Links

Note: For questions related to ITK, please use the official Discussion space: the issue tracker is reserved to track different aspects of the software development process, as highlighted by the available templates.

About

The Insight Toolkit (ITK) is an open-source, cross-platform toolkit for N-dimensional scientific image processing, segmentation, and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

The ITK project uses an open governance model and is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

ITK is distributed in binary Python packages. To install:

pip install itk

conda install -c conda-forge itk

The cross-platform, C++ core of the toolkit may be built from source using CMake.

Copyright

NumFOCUS holds the copyright of this software. NumFOCUS is a non-profit entity that promotes the use of open source scientific software for educational and research purposes. NumFOCUS delegates project governance to the Insight Software Consortium Council, an educational consortium dedicated to promoting and maintaining open-source, freely available software for medical image analysis. This includes promoting such software in teaching, research, and commercial applications, and maintaining webpages and user and developer communities. ITK is distributed under a license that enables use for both non-commercial and commercial applications. See LICENSE and NOTICE files for details.

Supporting ITK

ITK is a fiscally sponsored project of NumFOCUS, a non-profit dedicated to supporting the open source scientific computing community. If you want to support ITK's mission to develop and maintain open-source, reproducible scientific image analysis software for education and research, please consider making a donation to support our efforts.

NumFOCUS is 501(c)(3) non-profit charity in the United States; as such, donations to NumFOCUS are tax-deductible as allowed by law. As with any donation, you should consult with your personal tax adviser or the IRS about your particular tax situation.

Professional Services

Kitware provides professional services for ITK, including custom solution creation, collaborative research and development, development support, and training.

Citation

To cite ITK, please reference, as appropriate:

The papers

McCormick M, Liu X, Jomier J, Marion C, Ibanez L. ITK: enabling reproducible research and open science. Front Neuroinform. 2014;8:13. Published 2014 Feb 20. doi:10.3389/fninf.2014.00013

Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R. Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK – The Insight Toolkit. In Proc. of Medicine Meets Virtual Reality, J. Westwood, ed., IOS Press Amsterdam pp 586-592 (2002).

The books

Johnson, McCormick, Ibanez. "The ITK Software Guide: Design and Functionality." Fourth Edition. Published by Kitware, Inc. 2015 ISBN: 9781-930934-28-3.

Johnson, McCormick, Ibanez. "The ITK Software Guide: Introduction and Development Guidelines." Fourth Edition. Published by Kitware, Inc. 2015 ISBN: 9781-930934-27-6.

Specific software version

Once your work has been published, please create a pull request to add the publication to the ITKBibliography.bib file.

Project details

These details have been verified by PyPI

Maintainers

jcfr phcerdan tbirdso thewtex

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

This version

5.4.0

May 21, 2024

5.4rc4 pre-release

Apr 17, 2024

5.4rc3 pre-release

Apr 9, 2024

5.4rc2 pre-release

Oct 20, 2023

5.4rc1 pre-release

Jul 19, 2023

5.3.0

Nov 26, 2022

5.3rc4.post4 pre-release

Nov 14, 2022

5.3rc4.post3 pre-release

Sep 22, 2022

5.3rc4.post2 pre-release

Jul 28, 2022

5.3rc4.post1 pre-release

Jul 7, 2022

5.3rc4 pre-release

Apr 3, 2022

5.3rc3 pre-release

Jan 18, 2022

5.3rc2.post1 pre-release

Nov 3, 2021

5.3rc2 pre-release

Oct 29, 2021

5.3rc1 pre-release

Sep 13, 2021

5.2.1.post1

Sep 1, 2021

5.2.1

Aug 14, 2021

5.2.0.post3

May 28, 2021

5.2.0.post2

May 6, 2021

5.2.0.post1

Apr 29, 2021

5.2.0

Apr 4, 2021

5.2rc3 pre-release

Mar 5, 2021

5.2rc2 pre-release

Feb 12, 2021

5.2rc1 pre-release

Jan 6, 2021

5.1.2

Dec 2, 2020

5.1.1.post1

Oct 21, 2020

5.1.1

Aug 11, 2020

5.1.0.post3

Jun 15, 2020

5.1.0.post2

May 30, 2020

5.1.0.post1

May 26, 2020

5.1.0

Apr 27, 2020

5.1rc3 pre-release

Apr 2, 2020

5.1rc2 pre-release

Feb 21, 2020

5.1rc1.post1 pre-release

Dec 9, 2019

5.1rc1 pre-release

Dec 6, 2019

5.1b1 pre-release

Sep 4, 2019

5.0.1

Jul 24, 2019

5.0.0.post1

Jun 7, 2019

5.0.0

May 23, 2019

5.0rc2 pre-release

Apr 29, 2019

5.0rc1 pre-release

Feb 1, 2019

5.0b3 pre-release

Dec 21, 2018

5.0b1 pre-release

Sep 7, 2018

5.0a2 pre-release

May 25, 2018

5.0a1 pre-release

Apr 17, 2018

4.13.2.post1

Mar 21, 2019

4.13.2

Mar 6, 2019

4.13.1.post1

Aug 11, 2018

4.13.0

Dec 21, 2017

4.12.0.post1

Jun 15, 2017

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

itk_segmentation-5.4.0-cp311-abi3-win_amd64.whl (5.1 MB view details)

Uploaded May 21, 2024 CPython 3.11+ Windows x86-64

itk_segmentation-5.4.0-cp311-abi3-manylinux_2_28_x86_64.whl (15.9 MB view details)

Uploaded May 21, 2024 CPython 3.11+ manylinux: glibc 2.28+ x86-64

itk_segmentation-5.4.0-cp311-abi3-manylinux_2_28_aarch64.whl (14.6 MB view details)

Uploaded May 21, 2024 CPython 3.11+ manylinux: glibc 2.28+ ARM64

itk_segmentation-5.4.0-cp311-abi3-manylinux_2_17_x86_64.whl (14.8 MB view details)

Uploaded May 21, 2024 CPython 3.11+ manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp311-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB view details)

Uploaded May 21, 2024 CPython 3.11+ manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp311-abi3-macosx_11_0_arm64.whl (12.6 MB view details)

Uploaded May 21, 2024 CPython 3.11+ macOS 11.0+ ARM64

itk_segmentation-5.4.0-cp311-abi3-macosx_10_9_x86_64.whl (13.1 MB view details)

Uploaded May 21, 2024 CPython 3.11+ macOS 10.9+ x86-64

itk_segmentation-5.4.0-cp310-cp310-win_amd64.whl (5.0 MB view details)

Uploaded May 21, 2024 CPython 3.10 Windows x86-64

itk_segmentation-5.4.0-cp310-cp310-manylinux_2_28_x86_64.whl (15.8 MB view details)

Uploaded May 21, 2024 CPython 3.10 manylinux: glibc 2.28+ x86-64

itk_segmentation-5.4.0-cp310-cp310-manylinux_2_28_aarch64.whl (14.6 MB view details)

Uploaded May 21, 2024 CPython 3.10 manylinux: glibc 2.28+ ARM64

itk_segmentation-5.4.0-cp310-cp310-manylinux_2_17_x86_64.whl (14.7 MB view details)

Uploaded May 21, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.3 MB view details)

Uploaded May 21, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp310-cp310-macosx_11_0_arm64.whl (12.3 MB view details)

Uploaded May 21, 2024 CPython 3.10 macOS 11.0+ ARM64

itk_segmentation-5.4.0-cp310-cp310-macosx_10_9_x86_64.whl (13.1 MB view details)

Uploaded May 21, 2024 CPython 3.10 macOS 10.9+ x86-64

itk_segmentation-5.4.0-cp39-cp39-win_amd64.whl (5.0 MB view details)

Uploaded May 21, 2024 CPython 3.9 Windows x86-64

itk_segmentation-5.4.0-cp39-cp39-manylinux_2_28_x86_64.whl (15.8 MB view details)

Uploaded May 21, 2024 CPython 3.9 manylinux: glibc 2.28+ x86-64

itk_segmentation-5.4.0-cp39-cp39-manylinux_2_28_aarch64.whl (14.6 MB view details)

Uploaded May 21, 2024 CPython 3.9 manylinux: glibc 2.28+ ARM64

itk_segmentation-5.4.0-cp39-cp39-manylinux_2_17_x86_64.whl (14.7 MB view details)

Uploaded May 21, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.3 MB view details)

Uploaded May 21, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp39-cp39-macosx_11_0_arm64.whl (12.3 MB view details)

Uploaded May 21, 2024 CPython 3.9 macOS 11.0+ ARM64

itk_segmentation-5.4.0-cp39-cp39-macosx_10_9_x86_64.whl (13.1 MB view details)

Uploaded May 21, 2024 CPython 3.9 macOS 10.9+ x86-64

itk_segmentation-5.4.0-cp38-cp38-win_amd64.whl (5.0 MB view details)

Uploaded May 21, 2024 CPython 3.8 Windows x86-64

itk_segmentation-5.4.0-cp38-cp38-manylinux_2_28_x86_64.whl (15.8 MB view details)

Uploaded May 21, 2024 CPython 3.8 manylinux: glibc 2.28+ x86-64

itk_segmentation-5.4.0-cp38-cp38-manylinux_2_28_aarch64.whl (14.6 MB view details)

Uploaded May 21, 2024 CPython 3.8 manylinux: glibc 2.28+ ARM64

itk_segmentation-5.4.0-cp38-cp38-manylinux_2_17_x86_64.whl (14.7 MB view details)

Uploaded May 21, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.3 MB view details)

Uploaded May 21, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

itk_segmentation-5.4.0-cp38-cp38-macosx_10_9_x86_64.whl (13.1 MB view details)

Uploaded May 21, 2024 CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file itk_segmentation-5.4.0-cp311-abi3-win_amd64.whl.

File metadata

Download URL: itk_segmentation-5.4.0-cp311-abi3-win_amd64.whl
Upload date: May 21, 2024
Size: 5.1 MB
Tags: CPython 3.11+, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for itk_segmentation-5.4.0-cp311-abi3-win_amd64.whl
Algorithm	Hash digest
SHA256	`45374d3c38884ed02e0dfebf73f363e265184a50f7c46d55d8c9217e690a571f`
MD5	`6024f1d9df7e81dc32d4588de497db88`
BLAKE2b-256	`778d916c078544ac29a5ac215d2e5a2b07c9314b515b63136be92d6856a41f55`

Algorithm	Hash digest
SHA256	`ff2ca7cdf42c8c32b20e1ecf7ee0a0be68e885ce673c565e72a4876b28d2e2f3`
MD5	`aeb78230b4d173a9b83e1fcddf7a7812`
BLAKE2b-256	`1ae594a596fba3dbecdea33059b455294fbfea91fcfc0d6cfd92372e10a6da93`

Algorithm	Hash digest
SHA256	`8dd071dbb1ba1c8fcf26a4011fc3b7069d444e10bc56279d7b26fd3193ac58aa`
MD5	`779a11e9d7da0f4414087f806544e2f1`
BLAKE2b-256	`79b9ba9598bb8b7ee3962980d481065a58f75569c5a2df1d01520083288894b0`

Algorithm	Hash digest
SHA256	`728282cf1e05f535679b113cc5901e26c75a3ac9fba380de63cb55ea6df45f4b`
MD5	`57e0eb57aba0f54d42e0718261c1aac7`
BLAKE2b-256	`ff1172bcfa9729f882a1291aa886d614b5ed12ae6c75cd8313539ea86c383d62`

Algorithm	Hash digest
SHA256	`643a3d032e02f7cf426b2a9b462fa8d60f0a52e0ab6b064bef2870917a91cb28`
MD5	`638d173f321dfe8242006a053c573a08`
BLAKE2b-256	`d7cf386ca7e914ec240ce801e03e5ebd0990f739e198238fcfae798bae72b008`

Algorithm	Hash digest
SHA256	`b3c154e7a92be2ad48e6bf8bd5a8a17610f890bb420abb54dd2bde15b1c9c52c`
MD5	`cf04e981942000cc0ca2885195c90034`
BLAKE2b-256	`3a927d32413898fe4068f0b2da97ab783057e6af84123aa6c54af3706476fed9`

Algorithm	Hash digest
SHA256	`27a8b53ff2c817d876c612ad3ae810219f7a54e5bf6f3c30cd76730e7c7f3862`
MD5	`5fb7c5e304986fb8dafa2a5adb08d403`
BLAKE2b-256	`60ef9c0e583aa7aa9afa5218187abb545be66147536335fc358fbf7a974b120f`

Algorithm	Hash digest
SHA256	`3aedab2d725db8151e228772fc8456f782372d254df2267a2b39b2fbf46583e1`
MD5	`d7e61702079d630c622f4ce871bd8733`
BLAKE2b-256	`777ebcb113a3489ca848c52a00ae2442010726d1b2993c1b64ab6dd2fc0e70d3`

Algorithm	Hash digest
SHA256	`01b5aeaafff4e1a89dd74656087358a1b3d5dc5bed761aefedf8e90a962205dc`
MD5	`6a9dbc84462d4d32d2abc0513376643f`
BLAKE2b-256	`65923846d4280dc60ecc8bdad62265385938c5ae04da33f20ca8ea24418a98dc`

Algorithm	Hash digest
SHA256	`129b2e99dd4e6c8b7911e8124eeaafeb7bc416469219e82a90372e1021afe1ce`
MD5	`1a6e7e1b0167b474858c849b2c49931b`
BLAKE2b-256	`a761662e72bf560c96d308c4d48b1b21e1a040eda9e8884c71dbc40ac8d70a9c`

Algorithm	Hash digest
SHA256	`6b87842950edd3e713b62136d833ef728c5fe260fbf46b3e35c4477a97fac556`
MD5	`a66a3c880043af4caaf7ec48de4b25dd`
BLAKE2b-256	`0120d04a7d4a4987a7ef097936f43c42b54e5c095ccf8f0850cdc2fd09bc19db`

Algorithm	Hash digest
SHA256	`10b652acc227f5c6e9720d852a8da8d71bcc57a511e9b47b9b56a3eb9d5d8ae5`
MD5	`8406c5853a30869b3bcaedc4135d6ace`
BLAKE2b-256	`857232e21844d527f30b7e896d9775b5d97d59bedb72cab8f4976c3a5f7d11f7`

Algorithm	Hash digest
SHA256	`21e274d65ce1b19a21d5eed839ccf4a650a4e444fffb51f46b50567781b10ba9`
MD5	`19e7a3d3dd5b62fd9093b449181a3eba`
BLAKE2b-256	`df9fe4f0f053cb8865bab147dcb5fa4744a9570aa6db823739347f35ce952b83`

Algorithm	Hash digest
SHA256	`425b366f73060fb490775ef679dcd18096f639f9786202088947498e9fbd62e4`
MD5	`23a2932cc51a0a46425f6af1d8f09984`
BLAKE2b-256	`8878db3a7e33be6c2d03bbb7f7ebf124d9c0a219d9ae20f6798e7f738203eea4`

Algorithm	Hash digest
SHA256	`fbf83e7e2e79680cda9edbc394de29b1fd4fb9a626583ae8e79a9247fbeb05bf`
MD5	`7ae8d8ccbef2d7704456184b0d39672b`
BLAKE2b-256	`4340eee28dc7b383be22b6bffb48c0e6dadb126bdf38b6310a9ca4d4acd3d4ab`

Algorithm	Hash digest
SHA256	`cbf2a36ca7ee55c963f471cde5857c6de505668d52d40d1ef1723e18067cb4e2`
MD5	`23040675fad566c165ceef3553b66604`
BLAKE2b-256	`7343eb0a8758302750310e118d19fdee571905cdce94f0acbbc55fb1fd2f83a0`

Algorithm	Hash digest
SHA256	`45f05fdf709d3a31e6f4dac9c498ecbc247e03985fac9a68b4cb4c6b8ee53d39`
MD5	`c1968410eae62f4023023af6527aa8e4`
BLAKE2b-256	`334163ce52c5a24c74ba93872181655632818fe78e60744143502795884cce2a`

Algorithm	Hash digest
SHA256	`911d1140ae1538b4728997ae4cf5ae057090abe51951b0f0cac2ecf7ebb7a330`
MD5	`ec1762b6ebace9ec7176bb060c6fb272`
BLAKE2b-256	`2b8308f6595050fed8fc6aed3a3e4168b5f6574d9359cf867a50bab42a5de566`

Algorithm	Hash digest
SHA256	`d89dc2adcb0c5b805303d7513775e22b9f88830cdd46c7c2ae54585632b7b417`
MD5	`b2280b8b67d587d376d2d9c1306c7774`
BLAKE2b-256	`f58292795869d985784cd1d7ae9fd4a6742f176ce783089b9dc76029a6f2f6e0`

Algorithm	Hash digest
SHA256	`4220119602381c77c6ba8850a3774c1316f82b84247ffd8a33708022d926f817`
MD5	`752dc7cfe065a384e3fbbe05a5908413`
BLAKE2b-256	`ab5806b6454049e4663421f61b43788cb569a36322e5fbc2076d4e5251b0eb55`

Algorithm	Hash digest
SHA256	`9b86f0e1b68e1511015a3b55934a2971cb2ffdbf67123c3c7c26b012fe6b2ad6`
MD5	`cd8866dfcef5b522c39b4ab4a694f208`
BLAKE2b-256	`d2acbc844f2752c1f754650d84d18db9cb5de6e83570861a1eb66b7120db7a48`

itk-segmentation 5.4.0

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

ITK: The Insight Toolkit

Links

About

Copyright

Supporting ITK

Professional Services

Citation

The papers

The books

Specific software version

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

Algorithm	Hash digest
SHA256	`33383716bd0e76a24c322bcdedf6f2472940243f42b1ef7d713094af317d842f`
MD5	`e947ec7c30a2e3e020c0a78871a2a13c`
BLAKE2b-256	`834b97d30e768d11ca6b279117f17252b9b07b036c76c22534c537ddf17c08b4`

Algorithm	Hash digest
SHA256	`b4bd699465aa43600e845d62532d17d9bf7965a4ca971fbc27a425c15a6fbebd`
MD5	`3c323e152649216e7276551d77be31a9`
BLAKE2b-256	`e7cf16a1b8addcc1c348253d632f00163123436cda511769a270991bbc133989`

Algorithm	Hash digest
SHA256	`26a3acbb5fff3cd9346daa9630fbd20c74da2b364d112e19881780de46d42bf5`
MD5	`12285e888e079ab59a4951247083d767`
BLAKE2b-256	`1582ea6f52a9d0d57e56eb558fdb737b2c1d6cfdb48a3b32c37394b9bbd22344`

Algorithm	Hash digest
SHA256	`cee07977cfe8b7e8ad71497378128371b29eb13766d4aa6e443b7a33227cd902`
MD5	`b0818b8bcd9713bc65d09ab4ea1a6d9b`
BLAKE2b-256	`2b9fcb14f70621efd90301dcb5dc99da1b5e9a719f3a5e10345369ba5d24daf0`

Algorithm	Hash digest
SHA256	`8edc5c2884b5bd111c030aa61ca44ec4c30d39e0d6d1ce2448fac4d614a00581`
MD5	`622f205be808869e34b00dafd90920c8`
BLAKE2b-256	`3665fa1a1f20c2fa76f09d859fc0a48e411e2533ea91d29bc6964494b29ba385`

Algorithm	Hash digest
SHA256	`89eaf2bf3d683296e8b9ab6dcf12e58580d51ac37e9ee11b338aff3e2f9adaee`
MD5	`cb325117442b78f971c1ad51ab43c288`
BLAKE2b-256	`6f4c656b270716139d221eab0a07b7dfc62016b754c18fae035eb7ac57fd63da`