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| − | | + | [[Documentation/4.8/Modules/FastGrowCut|FastGrowCut]] extension has been completely reworked, hugely improved in performance, robustness, and usability, and moved to a built-in effect of Segment editor module named as [https://slicer.readthedocs.io/en/latest/user_guide/module_segmenteditor.html#grow-from-seeds-grow-from-seeds Grow from seeds] effect. |
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| − | {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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| − | {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}
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| − | 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 [http://www.na-mic.org/ NA-MIC website].<br>
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| − | Author: Liangjia Zhu, Stony Brook University<br>
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| − | Contributor: Ivan Kolesov, Stony Brook University<br>
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| − | Contributor: Yi Gao, University of Alabama Birmingham<br>
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| − | Contributor: Peter Karasev, Georgia Institute of Technology<br>
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| − | Contributor: Ron Kikinis, BWH <br>
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| − | Contributor: Allen Tannenbaum, Stony Brook University<br>
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| − | Contact: Liangjia Zhu, <email>liangjia.zhu@stonybrook.edu</email><br>
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| − | <!--Contact: FIRSTNAME LASTNAME, <email>john@doe.org</email><br> -->
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| − | {{documentation/{{documentation/version}}/module-introduction-row}}
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| − | {{documentation/{{documentation/version}}/module-section|Module Description}}
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| − | 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.
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| − | <!--Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.
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| − | {{documentation/{{documentation/version}}/module-section|Tutorials}}
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| − | Step 1.) Add data volume to segment
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| − | [[File:LoadMeningioma.png|500px]]
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| − | 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
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| − | [[File:StartEditorMeningioma.png|500px]]
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| − | Step 3.) Select the “CarreraSlice” effect in the “Edit Selected Label Map” drop-down menu
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| − | [[File:CarreraSliceEffect.png|50px]]
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| − | 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)
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| − | [[File:StartBotMeningioma.png|500px]]
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| − | Step 5.) Turn “On” all three slice views in the 3D Plane
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| − | [[File:TurnOnSlices3DMeningioma.png|500px]]
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| − | Step 6.) Initialize the segmentation using fast GrowCut
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| − | * (a) go to PaintEffect to draw seed regions (label 1 for foreground and 2 for background), then press 'G' to run fast GrowCut.
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| − | [[File:FGCSeed.png|500px]] [[File:FGCSeg1.png|500px]]
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| − | * (b) If not satified, press 'S' to toggle between seed image and segmentation result. Edit on the seed image to reduce over/under segmentaions.
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| − | [[File:FGCSeed2.png|500px]]
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| − | * (c) Once finished editing on the seed image, press 'G' to run fast GrowCut again.
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| − | [[File:FGCSeg2.png|500px]]
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| − | The steps 6 (b) and (c) may be repeated a couple of times until satisfied.
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| − | Step 7.) Once satisfied with the initialization, press 'M' to start KSlice interactive segmentation. The energy functionals available are:
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| − | * (a) press 'F' for local-global Chan-Vese segmentation
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| − | * (b) press 'U' for mean curvature smoothing
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| − | * (c) press 'E' for Chan-Vese segmentation
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| − | {{documentation/{{documentation/version}}/module-section|Similar Modules}}
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| − | {{documentation/{{documentation/version}}/module-section|Information for Developers}}
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