Documentation/Nightly/Modules/FastGrowCut - Revision history

Lasso at 17:00, 5 November 2018

2018-11-05T17:00:49Z

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← Older revision		Revision as of 17:00, 5 November 2018
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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.		[[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/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|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).
+* 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.
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 {{documentation/{{documentation/version}}/module-section|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).
+* 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).
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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)
+Step 4.) Press the “Start Fast GrowCut” button ( FastGrowCut is now running in the background until the “Stop FastGrowCut” button is pressed)
 [[File:StartBotMeningioma.png|500px]]
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 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:
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-* (a) press 'F' for local-global Chan-Vese segmentation
+{{documentation/{{documentation/version}}/module-section|Multi-Label Segmentation Examples}}
-* (b) press 'U' for mean curvature smoothing
+The module supports multi-label segmentations. Two examples are shown below.
 * Brain ventricle and tumor segmentation
 [[File:FGC_Brain_Seed.png|500px]]

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 * Brain ventricle and tumor segmentation
 [[File:FGC_Brain_Seed.png|500px]]
 ) Seed image
 [[File:FGC_Brain_Seg.png|500px]]
 ) Segmentation results
 * Heart chamber segmentation
 [[File:FGC_Heart_Seed.png|500px]]
 ) Seed image
 [[File:FGC_Heart_Seg.png|500px]]
 ) Segmentation results
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 {{documentation/{{documentation/version}}/module-section|Similar Modules}}