Documentation/Nightly/Modules/FastGrowCut

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For the stable Slicer documentation, visit the 4.8 page.




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,






Module Description

This is a fast implementation of the GrowCut method. It supports multi-label segmentation and user online interactions. Please see the references below for more details.








Tutorials

Step 1.) Add data volume to segment

LoadMeningioma.png


Step 2.) Go to the “Editor” module, select the volume loaded in Step 1 as the “Master Volume” in the “Create and Select Label Maps” drop-down menu

StartEditorMeningioma.png


Step 3.) Select the “FastGrowCutEffect” effect in the “Edit Selected Label Map” drop-down menu

FastGrowCutEffect.png


Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)

StartBotMeningioma.png


Step 5.) Turn “On” all three slice views in the 3D Plane

TurnOnSlices3DMeningioma.png

Step 6.) Initialize the segmentation using fast GrowCut

  • (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.

FGCSeed.png FGCSeg1.png

  • (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions.

FGCSeed2.png

  • (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again.

FGCSeg2.png

The steps 6 (b) and (c) may be repeated a couple of times until satisfied.

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.

Information for Developers