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/tubetklib: 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/tubetklib. 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/apps: These are the command-line interface (CLI) equivalents to the methods available via TubeTK/include. This is intended for bash, bat, and other system-call scripts. The level of modularization and intended users are similar to those of TubeTK/include. C++ and python-based CLIs are provided. Continuous, unit-level testing of TubeTK/include is provided via these applications.

Compling ITKTubeTK requires that you have CMake 3.11 or later.

There are two dependencies that must be compiled first

1. ITK
2. SlicerExecutionModel

Additionally, you may want to compile

3. VTK

to enable the "sliding organ registration" (anisotropic diffusion regularization and registration) methods in ITKTubeTK.

1) ITK

For ITK, we want to compile ITK v5.0 or later. Begin by checking out ITK's source

$ cd /                               (To keep paths short, start at a top-level dir)
$ mkdir src
$ cd src
$ git clone https://github.com:/InsightSoftwareConsortium/ITK.git -b Release
$ mkdir ITK-Release
$ cmake-gui ../ITK

Using CMake, you should configure ITK with the following options

• CMAKE_BUILD_TYPE = Release (This is an advanced option)
• ITK_LEGACY_SILENT = On (This is an advanced option)
• ITK_WRAP_PYTHON = On (Note: Only Python3 is supported at this time)
• Module_MinimalPathExtraction = On (This is an advanced option)
• You may also want to turn off building Tests and Examples

Once you have configured and generated your build files (e.g., for make or ninja or whatever):

$ ninja  (or make, or nmake, or whatever is appropriate for your system)

This build process can take many hours on a Windows PC, because it is generating the files needed to use ITK with Python, as well as the standard C++ libraries, applications, examples, and tests.

2) SlicerExecutionModel

After compiling ITK, we repeat the build process for SlicerExecutionModel (used by ITKTubeTK applications):

$ cd /src
$ git clone https://github.com:/Slicer/SlicerExecutionModel.git
$ mkdir SlicerExecutionModel-Release
$ cd SlicerExecutionModel-Release
$ cmake-gui ../SlicerExecutionModel

Using CMake, you should configure SlicerExecutionModel as follows:

• CMAKE_BUILD_TYPE = Release
• ITK_DIR = /src/ITK-Release

Once SlicerExecutionModel's cmake files are configured and build files are generated, you should build the application:

$ ninja  (or make, or nmake, or whatever is appropriate for your system)

This build should be relatively quick.

3) VTK

Optionally, you may also want to build VTK. This is used by the Sliding Organ Registration algorithm (anisotropic diffusion regularization and registration).

$ cd /src
$ git clone https://github.com:/Kitware/VTK.git
$ mkdir VTK-Release
$ cd VTK-Release
$ cmake-gui ../VTK

Using CMake, you should configure VTK as follows:

• CMAKE_BUILD_TYPE = Release
• VTK_PYTHON_VERSION = 3
• VTK_WRAP_PYTHON = True
• You may also want to turn off building Tests and Examples

After configuring CMake and generating the build files, you should compile VTK:

$ ninja  (or make, or nmake, or whatever is appropriate for your system)

...And now you are ready to compile ITKTubeTK

Once ITK and SlicerExecutionModel have been compiled as described above, you can compile ITKTubeTK:

$ cd /src
$ git clone https://github.com:/KitwareMedical/ITKTubeTK
$ mkdir ITKTubeTK-Release
$ cd ITKTubeTK-Release
$ cmake-gui ../ITKTubeTK

Then we configure the CMake variables for ITKTubeTK

• CMAKE_BUILD_TYPE = Release
• ITK_DIR = /src/ITK-Release
• TubeTK_BUILD_APPLICATIONS = On
• SlicerExecutionModel_DIR = /src/SlicerExecutionModel-Release
• TubeTK_WRAP_PYTHON = On
• Optionally, if you have built VTK, you can enable the use of VTK and point VTK_DIR to your VTK-Release directory.

Then configure and generate you build files using cmake, and compile

$ ninja

Now you will want to add ITKTubeTK's applications to your command-line PATH. The directory to include in that path is:

/src/ITKTubeTK/bin

First, to be able to run all the python tests and examples, the following packages are required:

• numpy
• scipy
• jupyter
• IPython
• tornado
• pyzmq
• jinja2
• tables
• matplotlib
• pyqtgraph
• PyOpengl
• PySide

Installing most required packages can be done with the following command line:

$ pip install requirements.txt

Second, you will need to add the modules of python-wrapped ITK and ITKTubeTK to your python environment. This is accomplished by copying the files that specify the paths to their python modules into your python site-packages directory.

To find the site-packages directory on your system, follow the directions on this link: https://stackoverflow.com/questions/122327/how-do-i-find-the-location-of-my-python-site-packages-directory

Let's assume that path is /Python/Python36/site-packages.

First you will want to copy ITK's python paths file into that directory

$ cp ~/src/ITK-Release/Wrapping/Generators/Python/WrapITK.pth /Python/Python36/site-packages

Then you will want to add ITKTubeTK's paths file ONTO that file

$ cat ~/src/ITKTubeTK-Release/Wrapping/Generators/Python/WrapITK.pth >> /Python/Python36/site-packages/WrapITK.pth

Then you can test your configuration:

$ python -c "import itk"

and

$ python -c "from itk import TubeTK"

Both of the above commands should execute and return without errors.

The development of TubeTK is supported in part by the

• NCI under award numbers R01CA138419, R01CA170665, R43CA165621, and R44CA143234;
• NIBIB (NBIB) of the National Institutes of Health (NIH) under award numbers R01EB014955, R41EB015775, R43EB016621, and U54EB005149;
• NIBIB and NIGMS R01EB021396;
• NINDS R42NS086295 and R41NS081792;
• Defense Advanced Research Projects Agency (DARPA) under the TRUST program.

This software is distributed under the Apache 2.0 license. Please see the LICENSE file for details.

Publications

( See also [http://www.aylward.org/biosketch/publications-1] )

• D.F. Pace, S.R. Aylward, M. Niethammer, "A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs," Medical Imaging, IEEE Transactions on , vol.32, no.11, pp.2114,2126, Nov. 2013 doi: 10.1109/TMI.2013.2274777
• E. Bullitt, D. Zeng, B. Mortamet, A. Ghosh, S. R. Aylward, W. Lin, B. L. Marks, and K. Smith, "The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography," NEUROBIOLOGY OF AGING, vol. 31, no. 2, pp. 290-300, Feb. 2010.
• E. Bullitt, M. Ewend, J. Vredenburgh, A. Friedman, W. Lin, K. Wilber, D. Zeng, S. R. Aylward, and D. Reardon, "Computerized assessment of vessel morphological changes during treatment of glioblastoma multiforme: Report of a case imaged serially by MRA over four years," NEUROIMAGE, vol. 47, pp. T143-T151, Aug. 2009.
• E. Bullitt, K. Muller, I. Jung, W. Lin, and S. Aylward, "Analyzing attributes of vessel populations," MEDICAL IMAGE ANALYSIS, vol. 9, no. 1, pp. 39-49, Feb. 2005.
• S. Aylward, J. Jomier, S. Weeks, and E. Bullitt, "Registration and analysis of vascular images," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 55, no. 2-3, pp. 123-138, Dec. 2003.
• E. Bullitt, G. Gerig, S. Pizer, W. Lin, and S. Aylward, "Measuring tortuosity of the intracerebral vasculature from MRA images," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 22, no. 9, pp. 1163-1171, Sep. 2003.
• 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.
• S. Aylward, S. Pizer, D. Eberly, and E. Bullitt, "Intensity Ridge and Widths for Tubular Object Segmentation and Description," in MMBIA '96: Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96), Washington, DC, USA, 1996, p. 131.