Documentation/Nightly/Extensions/BrainVolumeRefinement - Revision history

Murta at 23:50, 29 June 2022

2022-06-29T23:50:49Z

Murta at 23:49, 29 June 2022

2022-06-29T23:49:27Z

Acsenrafilho at 07:15, 14 September 2019

2019-09-14T07:15:10Z

Acsenrafilho at 06:54, 14 September 2019

2019-09-14T06:54:15Z

Acsenrafilho at 18:14, 6 September 2019

2019-09-06T18:14:22Z

Acsenrafilho: Created page with "{{documentation/versioncheck}} {{documentation/{{documentation/version}}/module-header}}

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-* da Silva Senra Filho, A.C., Simozo, F.H. & Junior, L.O.M. Brain volume refinement (BVeR): automatic correction tool as an alternative to manual intervention on brain segmentation. Res. Biomed. Eng. 37, 631–640 (2021). https://doi.org/10.1007/s42600-021-00168-x
+* da Silva Senra Filho, A.C., Simozo, F.H. & Murta Junior, L.O. Brain volume refinement (BVeR): automatic correction tool as an alternative to manual intervention on brain segmentation. Res. Biomed. Eng. 37, 631–640 (2021). https://doi.org/10.1007/s42600-021-00168-x
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-* Insight-Journal
+* da Silva Senra Filho, A.C., Simozo, F.H. & Junior, L.O.M. Brain volume refinement (BVeR): automatic correction tool as an alternative to manual intervention on brain segmentation. Res. Biomed. Eng. 37, 631–640 (2021). https://doi.org/10.1007/s42600-021-00168-x
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 Repositories:
 * Source code: [https://github.com/CSIM-Toolkits/SlicerBrainVolumeRefinement GitHub repository]
-* Issue tracker:  [https://github.com/CSIM-Toolkits/SlicerBrainVolumeRefinement/issues open issues and enhancement requests]
+*Source code: [https://github.com/CSIM-Toolkits/SlicerBrainVolumeRefinement GitHub repository]
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-*[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion|Gradient Anisotropic Diffusion]]
+* NA
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 Repositories:
-* Source code: [https://github.com/CSIM-Toolkits/... GitHub repository]
+* Source code: [https://github.com/CSIM-Toolkits/SlicerBrainVolumeRefinement GitHub repository]
-* Issue tracker:  [https://github.com/CSIM-Toolkits/.../issues open issues and enhancement requests]
+* Issue tracker:  [https://github.com/CSIM-Toolkits/SlicerBrainVolumeRefinement/issues open issues and enhancement requests]
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 Most frequently used for these scenarios:
 * Use Case 1: Cortical thickness surface delineation.
-**When dealing with grey-matter overestimate due to badly  brain extraction step.
+**When dealing with grey-matter overestimate due to badly brain extraction step.
 * Use Case 2: Brain atrophy
 **Assist in the total brain volume estimate also reducing the non-brain tissues belonging outside the grey-matter tissue frontier.
 <gallery widths="400px" heights="400px" perrow="3">
-Image:image1.png|Raw T1 weighted MRI Image
+Image:T1-FS.png|T1 weighted MRI Image with FreeSurfer original brain mask overlay (only the out surface is represented)
 </gallery>

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 [[Image:BVeR-logo.png|left]]
-The Brain Volume Refinement (BVeR) extension is designed to assist biomedical studies that uses MRI structural images of the healthy brain. The method is ... <ref>Tsallis, C. (2009). Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer.</ref>.
+The Brain Volume Refinement (BVeR) extension is designed to assist neuroscience studies. The BVeR algorithm is suitable for a broad use of healthy brain structural MRI images, e.g. T1w and T2w, offering broad application in many large data analyses. The main contribution of the proposed method is related to the reduction of manual interference in the brain volume refinement after an automatic skull stripping procedure been performed, helping to reduce human errors and processing time. Even though the BVeR method does not provide a fully brain extraction algorithm, it can be helpful as a ''ad hoc'' image processing step in which increase the quality of well-known brain extraction algorithm in the literature. Any brain extracting frameworks can be refined with this method, e.g. FSL-BET, FreeSurfer, BEasT, 3DSkullStrip, ROBEX, OptiBET and many others.
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 {{documentation/{{documentation/version}}/module-section|Use Cases}}
 Most frequently used for these scenarios:
-* Use Case 1: Noise reduction as a pre-processing step for tissue segmentation
+* Use Case 1: Cortical thickness surface delineation.
-**When dealing with single voxel classification schemes, a noise reduction pre-processing step is usually helpful to reduce data fluctuation due to acquisition artifacts (e.g. reducing the number of misclassified voxels).
+**When dealing with grey-matter overestimate due to badly  brain extraction step.
-* Use Case 2: Volume rendering
+* Use Case 2: Brain atrophy
-**Noise reduction will result in nicer looking volume renderings
+**Assist in the total brain volume estimate also reducing the non-brain tissues belonging outside the grey-matter tissue frontier.
-* Use Case 3: Noise reduction as part of image processing pipeline
 <gallery widths="400px" heights="400px" perrow="3">
-Image:MRI_raw.png|Raw T1 weighted MRI Image
+Image:image1.png|Raw T1 weighted MRI Image
 </gallery>
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-* da S Senra Filho, A.C., Garrido Salmon, C.E. & Murta Junior, L.O., 2015. Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), pp.2355–2373. DOI: 10.1088/0031-9155/60/6/2355
+* Insight-Journal
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 Repositories:
-* Source code: [https://github.com/CSIM-Toolkits/AnomalousFiltersExtension GitHub repository]
+* Source code: [https://github.com/CSIM-Toolkits/... GitHub repository]
-* Issue tracker:  [https://github.com/CSIM-Toolkits/AnomalousFiltersExtension/issues open issues and enhancement requests]
+* Issue tracker:  [https://github.com/CSIM-Toolkits/.../issues open issues and enhancement requests]
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