Difference between revisions of "Documentation/Nightly/Extensions/BrainVolumeRefinement"

From Slicer Wiki
Jump to: navigation, search
(Created page with "<noinclude>{{documentation/versioncheck}}</noinclude> <!-- ---------------------------- --> {{documentation/{{documentation/version}}/module-header}} <!-- --------------------...")
 
Line 29: Line 29:
 
[[Image:BVeR-logo.png|left]]
 
[[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.
−
 
 
−
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. Although not all the spectrum of skull stripping algorithm were not coverage in this study, we believe that a similar outcome can be generalized for many other brain extraction frameworks such as BEasT, 3DSkullStrip, ROBEX, OptiBET and many others. Finally, a diverse image post-processing analysis that are sensible to the brain volume estimate could also be improved by better tissue segmentation, e.g. cortical thickness and brain atrophy, which provide a valuable incentive to many biomedical studies.
 
  
 
<!-- ---------------------------- -->
 
<!-- ---------------------------- -->
Line 40: Line 38:
 
{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
Most frequently used for these scenarios:
 
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
+
 
−
**Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation
 
 
<gallery widths="400px" heights="400px" perrow="3">
 
<gallery widths="400px" heights="400px" perrow="3">
−
Image:MRI_raw.png|Raw T1 weighted MRI Image
+
Image:image1.png|Raw T1 weighted MRI Image
−
Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
 
−
Image:MRI_IAD.png|T1 weighted MRI Image with IAD filter (q=1.2)
 
−
Image:DTI_FA_raw.png|DTI-FA map without image filtering process
 
−
Image:DTI_FA_AAD.png|DTI-FA map with AAD image filtering (q=0.4)
 
 
</gallery>
 
</gallery>
  
Line 60: Line 53:
 
<!-- ---------------------------- -->
 
<!-- ---------------------------- -->
 
{{documentation/{{documentation/version}}/extension-section|References}}
 
{{documentation/{{documentation/version}}/extension-section|References}}
−
* 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
−
* Filho, A.C. da S.S. et al., 2014. Anisotropic Anomalous Diffusion Filtering Applied to Relaxation Time Estimation in Magnetic Resonance Imaging. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 3893–3896.
 
−
* Filho, A.C. da S.S., Barizon, G.C. & Junior, L.O.M., 2014. Myocardium Segmentation Improvement with Anisotropic Anomalous Diffusion Filter Applied to Cardiac Magnetic Resonance Imaging. In Annual Meeting of Computing in Cardiology.
 
−
* Filho, A.C. da S.S. et al., 2014. Brain Activation Inhomogeneity Highlighted by the Isotropic Anomalous Diffusion Filter. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, pp. 3313–3316.
 
−
* Senra Filho, A.C. da S., Duque, J.J. & Murta, L.O., 2013. Isotropic anomalous filtering in Diffusion-Weighted Magnetic Resonance Imaging. I. E. in M. and B. Society, ed. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013, pp.4022–5.
 
  
 
<!-- ---------------------------- -->
 
<!-- ---------------------------- -->
Line 71: Line 60:
  
 
Repositories:
 
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]
  
 
<!-- ---------------------------- -->
 
<!-- ---------------------------- -->
 
{{documentation/{{documentation/version}}/extension-footer}}
 
{{documentation/{{documentation/version}}/extension-footer}}
 
<!-- ---------------------------- -->
 
<!-- ---------------------------- -->

Revision as of 18:14, 6 September 2019

Home < Documentation < Nightly < Extensions < BrainVolumeRefinement


For the latest Slicer documentation, visit the read-the-docs.


Introduction and Acknowledgements

This work was partially funded by CAPES and CNPq, Brazilian Agencies. Information on CAPES can be obtained on the CAPES website and CNPq website.
Authors: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Fabrício Henrique Simozo, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Prof. Luiz Otávio Murta Junior, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email>

CSIM Laboratory  
University of Sao Paulo  
CNPq Brazil  
CAPES Brazil  


Extension Description

BVeR-logo.png

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.

Modules

  • Structural T1w and T2w brain volume correction: BVeR

Use Cases

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.
  • 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.

  • Raw T1 weighted MRI Image

Similar Extensions

References

  • Insight-Journal

Information for Developers

Work in progress
 Section under construction.


Repositories:

Retrieved from "https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/BrainVolumeRefinement&oldid=61400"