Documentation/Nightly/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. 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

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

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

Step 4.) Set the “Radius” parameter, the "Number of Iterations" and press the “Start Interactive Segmentor” button ( CarreraSlice is now running in the background until the “Stop Interactive Segmentor” button is pressed)

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

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.

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

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

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

Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:

• (a) press 'F' for local-global Chan-Vese segmentation
• (b) press 'U' for mean curvature smoothing
• (c) press 'E' for Chan-Vese segmentation

Segmentation Examples

• Brain ventricle and tumor segmentation

1) Seed image

2) Segmentation results

• Heart chamber segmentation

1) Seed image

2) Segmentation results

References

• Liangjia Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, Allen Tannenbaum. An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut, Kolesovin International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Interactive Medical Image Computing Workshop, 2014 (submitted).