https://github.com/insightsoftwareconsortium/itkgrowcut

ITKGrowCut is a remote module for ITK. It segments a 3D image from user-provided foreground and background seeds.
https://github.com/insightsoftwareconsortium/itkgrowcut

imaging insight-toolkit itk itk-module

Last synced: 7 months ago
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ITKGrowCut is a remote module for ITK. It segments a 3D image from user-provided foreground and background seeds.

• Host: GitHub
• URL: https://github.com/insightsoftwareconsortium/itkgrowcut
• Owner: InsightSoftwareConsortium
• License: apache-2.0
• Created: 2021-04-21T16:10:53.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2025-03-11T22:14:51.000Z (11 months ago)
• Last Synced: 2025-06-30T14:25:26.195Z (7 months ago)
• Topics: imaging, insight-toolkit, itk, itk-module
• Language: C++
• Homepage:
• Size: 99.6 KB
• Stars: 14
• Watchers: 16
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
ITKGrowCut

=================================

.. image:: https://github.com/InsightSoftwareConsortium/ITKGrowCut/workflows/Build,%20test,%20package/badge.svg

    :alt:    Build Status

.. image:: https://img.shields.io/pypi/v/itk-growcut.svg

    :target: https://pypi.python.org/pypi/itk-growcut

    :alt: PyPI Version

.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg

    :target: https://github.com/InsightSoftwareConsortium/ITKGrowCut/blob/master/LICENSE

    :alt: License

Overview

--------

ITKGrowCut is a remote module for ITK. The main filter segments a 3D image from user-provided seeds.

The original idea was presented by `Vezhnevets and Konouchine

`_:

 | Vladimir Vezhnevets and Vadim Konouchine:

 | “GrowCut” – interactive multi-label N-D image segmentation by cellular automata.

 | In: Proc. Graphicon. (2005) 150–156

In 2011 Harini Veeraraghavan of Memorial Sloan Kettering Cancer Center provided an `implementation based on ITK

`_.

In 2014, Zhu et al. presented an `efficient approximation

`_:

 | Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum.

 | An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut

 | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI),

 | Interactive Medical Image Computing Workshop, 2014

Zhu et al. also provided an open source implementation as a Slicer plugin, `based on VTK

`_.

Since then, their implementation was integrated into Slicer, `refactored and improved

`_.

In this remote module we are building upon the improved variant from Slicer.

Acknowledgements

----------------

This software was developed in part by the Center for Integrative Biomedical Computing (CIBC), the

`Scientific Computing and Imaging (SCI) Institute `_ and

`Kitware `_.

Support came from the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH)

under grant numbers P41 GM103545 and R24 GM136986.