Documentation/4.3/Modules/FastGrowCut

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Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website.

Author: Liangjia Zhu, Stony Brook University
Contributor: Ivan Kolesov, Stony Brook University
Contributor: Yi Gao, University of Alabama Birmingham
Contributor: Peter Karasev, Georgia Institute of Technology
Contributor: Ron Kikinis, BWH
Contributor: Allen Tannenbaum, Stony Brook University
Contact: Liangjia Zhu, <email>liangjia.zhu@stonybrook.edu</email>






Module Description

This is a fast implementation of the GrowCut method. The algorithm uses example segmentation to create a full segmentation of the volume. It supports multi-label segmentation and user online interactions. Please see the references below for more details.


Tutorials

Click here for a tutorial on using the Fast GrowCut effect.

Multi-Label Segmentation Examples

The module supports multi-label segmentations. Two examples are shown below.

  • Brain ventricle and tumor segmentation

FGC Brain Seed.png

1) Seed image

FGC Brain Seg.png

2) Segmentation results

  • Heart chamber segmentation

FGC Heart Seed.png

1) Seed image

FGC Heart Seg.png

2) Segmentation results

Similar Modules

References

  • 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 (submitted).

Information for Developers