Skip to main content

An open-source toolkit, led by Kitware, Inc., for the segmentation, registration, and analysis of tubes and surfaces in images.

Project description

ITKTubeTK: Tubular Object Extraction, Registration, and Analysis

License

Build, test, package

Documentation Status

Available in C++ and Python for Linux, Windows, and MacOS.

Overview

TubeTK is an open-source toolkit for the segmentation, registration, and analysis of tubes and surfaces in images, developed by Kitware, Inc.

Tubes and surfaces, as generalized 1D and 2D manifolds in N-dimensional images, are essential components in a variety of image analysis tasks. Instances of tubular structures in images include blood vessels in magnetic resonance angiograms and b-mode ultrasound images, wires in microscopy images of integrated circuits, roads in aerial photographs, and nerves in confocal microscopy.

A guiding premise of TubeTK is that by focusing on 1D and 2D manifolds we can devise methods that are insensitive to the modality, noise, contrast, and scale of the images being analyzed and to the arrangement and deformations of the objects in them. In particular, we propose that TubeTK's manifold methods offer improved performance for many applications, compared to methods involving the analysis of independent geometric measures (e.g., edges and corners) or requiring complete shape models.

TubeTK offers various interface layers:

  • TubeTK/src: This is the algorithms library. It is the lowest level of access to the methods of TubeTK. It is only available via C++, and it requires considerable expertise to effectively combine and call its methods to do anything useful. Interfacing directly with these algorithms is not recommended and is not well supported. Unit-level testing is performed continuously on these methods.

  • TubeTK/include: This is the ITK interface to select methods in TubeTK/src. This level of interface is intended for ITK users and Python scripts writers. The methods exposed represent a level of modularization that invites experimentation, integration with other toolkits (e.g., Scikit-Learn), and development of processing pipelines that accomplish significant image analysis goals. The interface is available as an ITK Extension and thereby available via Python using Wrapped ITK.

  • TubeTK/examples/Applications: These are optional command-line interface applications. These applications are mostly also available via the TubeTK/include interface, and thereby are available via python. Expansion of ITK will focus on the TubeTK/include directory, and new applications will only rarely be added. These applications are built when the cmake options BUILD_EXAMPLES is enabled. These applications also require SlicerExecutionModel, see https://github.com/Slicer/SlicerExecutionModel.

Using TubeTK

Minimal $ python -c "from itk import TubeTK"

Recommended $ python -c "from itk import TubeTK as ttk"

Acknowledgements

If you find TubeTK to be useful for your work, please cite the following publication when publishing your work:

  • S. R. Aylward and E. Bullitt, "Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction," Medical Imaging, IEEE Transactions on, vol. 21, no. 2, pp. 61-75, 2002.

Project details


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_tubetk-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl (22.8 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

itk_tubetk-1.3.2-cp39-cp39-win_amd64.whl (8.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

itk_tubetk-1.3.2-cp39-cp39-manylinux_2_28_x86_64.whl (22.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ x86-64

itk_tubetk-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl (22.8 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

itk_tubetk-1.3.2-cp38-cp38-win_amd64.whl (8.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

itk_tubetk-1.3.2-cp38-cp38-manylinux_2_28_x86_64.whl (22.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.28+ x86-64

itk_tubetk-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl (22.8 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

itk_tubetk-1.3.2-cp37-cp37m-win_amd64.whl (8.8 MB view details)

Uploaded CPython 3.7m Windows x86-64

itk_tubetk-1.3.2-cp37-cp37m-manylinux_2_28_x86_64.whl (22.2 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.28+ x86-64

itk_tubetk-1.3.2-cp37-cp37m-macosx_10_9_x86_64.whl (22.8 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file itk_tubetk-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e515642dfe8fba63e7fa5035ba036c29ac41772b6cdf0b6b0451b6022f494fed
MD5 daf82070941bfe264bf05fb414f659fc
BLAKE2b-256 98b6f97e54d5d8cf85b3f152bdda012b862ad745cff3a25ff15d8bcd7c9d5e26

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 bee91af167dce784964d63fbb3e1c4c474074b9a15b1859487a0764660d49218
MD5 b4fc33d850d63c984be00cd21a1ecf81
BLAKE2b-256 1fd6c6724a65ce38c939e2907875c3821d6f0956b4489d2f178de90b494f30a7

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 baa4493564d1387a9058b565fad963e33c446b45dbe5bf543c416d06e8b829df
MD5 49600218c68f1f4f54c4529207e28f5d
BLAKE2b-256 1ec9294955c86b3211f2cf185e0d2e74d5bb1142215af787b0d39d761d2607a1

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1b14bc8eafdfb3bfd2d5f6f6952dd845c720f8af77223abe77a79adafd24bdaa
MD5 06194ef0455e5063bf7ebc567f5f336c
BLAKE2b-256 3d2906644e0184e78e56dcdc0c9eedf07e9a0236b98b81c772708cb4ecc037d2

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 632f6121e8df5852de4435cddf60fd5b64dce4d27c64ef85e13d09c265d96d0e
MD5 a57b56dd4204b7441c03cc131989db5d
BLAKE2b-256 a5f9724b91e2638bf0846bdc59611cd561599f9142a7f3a2e3a91627f954cc32

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 7227a2917df3f2d1a2ddab59d09050bb625a70129c35ae4f7bab4e8c26870d63
MD5 54cdc52a1fddd7a52c79bdbd9a3007ad
BLAKE2b-256 ef1eee3c7e1c78d40f21a045515aef9847a25340e99a10f5d1c59b4da456609c

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 35beffafa6faf33ab67513ec440da687b2981b2a340d95c43e76c9382058152b
MD5 deaf0e4e04d2606c552e7d690e185706
BLAKE2b-256 305c85a78b3286a6c163bc624c79510cf231b4a7ca05f6f67b9b07603028df14

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 4295bf36d5c0834085c5d455df374427fce981b3ab93676d6064150879ff6ff1
MD5 d3d546e8448d3c8831ba16db1e9c782b
BLAKE2b-256 b652902497c81d9196d23bf2464413ab28240a7a706f872ba3953ca1bada0249

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp37-cp37m-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp37-cp37m-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 963740cc3f1af4d678d95416b70ff87dde904e964de5d03d1e08f0ed28241829
MD5 4e647f4e9c12191b8883483ba5a60a15
BLAKE2b-256 77672f38f4d487c85cdab3228d6ed689d4f53aee89b0f98e102c7ed495e9c908

See more details on using hashes here.

File details

Details for the file itk_tubetk-1.3.2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for itk_tubetk-1.3.2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 456a74fa14a106e4cddb1960e98ca8bb21b08117d3cf6dca1b956507b92d2dff
MD5 4f781a5f45426c1121d3348076babade
BLAKE2b-256 d04c78b9117485d3a0593d7abfacc171f6b790c0c2e591b08bf3cb7a32dc23d1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page