2009 Jim Cummings - Revision history

imported>Psych 204: /* Results */

2009-12-11T21:13:23Z

Results

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	= Results =		= Results =

	All results were limited to a basic visual comparison between alternate meshes and activity patterns. No statistical analysis of the significance of noted differences between meshes or activity patterns ~~have~~ been conducted.		All results were limited to a basic visual comparison between alternate meshes and activity patterns. No statistical analysis of the significance of noted differences between meshes or activity patterns has been conducted.

	== Segmentation and ITK-Snap Meshes ==		== Segmentation and ITK-Snap Meshes ==

imported>Psych 204: /* A Comparison of Segmentation Performance: Amateur, Experienced, and Algorithm */

2009-12-11T21:12:22Z

A Comparison of Segmentation Performance: Amateur, Experienced, and Algorithm

← Older revision		Revision as of 21:12, 11 December 2009
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	= A Comparison of Segmentation Performance: Amateur, Experienced, and Algorithm =		= A Comparison of Segmentation Performance: Amateur, Experienced, and Algorithm =
	Segmentation of the brain scan images produced by functional magnetic resonance imaging is a fundamental first step for the production of functional activity maps. The most basic of modern segmentation techniques typically entails employing software that allows a user to differentially depict white matter, grey matter, cerebrospinal fluid from both one another and ~~from~~ other structures not of interest. In turn, the ability to accurately map functional data is dependent upon proper initial segmentation of images. Even the most minor of voxel ~~classifications~~ can results in a topologically incorrect model of the brain (Fischl, Liu, Dale, 2001). Therefore, the motivation for this project was two-fold: 1) to allow an amateur with no hands-on knowledge to gain experience with a fundamental process of functional imaging and 2) to compare how alternate segmentation performances may differentially affect both the resultant three-dimensional meshes and the ability to accurately map functional data. This project specifically compared the results of segmentations produced by the amateur, a more experienced individual (as segmentation skill is thought to increase with practice), and an auto-segmenting program.		Segmentation of the brain scan images produced by functional magnetic resonance imaging is a fundamental first step for the production of functional activity maps. The most basic of modern segmentation techniques typically entails employing software that allows a user to differentially depict white matter, grey matter, cerebrospinal fluid from both one another and other structures not of interest. In turn, the ability to accurately map functional data is dependent upon proper initial segmentation of images. Even the most minor of mistakes in voxel classification can results in a topologically incorrect model of the brain (Fischl, Liu, Dale, 2001). Therefore, the motivation for this project was two-fold: 1) to allow an amateur with no hands-on knowledge to gain experience with a fundamental process of functional imaging and 2) to compare how alternate segmentation performances may differentially affect both the resultant three-dimensional meshes and the ability to accurately map functional data. This project specifically compared the results of segmentations produced by the amateur, a more experienced individual (as segmentation skill is thought to increase with practice), and an auto-segmenting program.
	<br>		<br>

imported>Psych 204: /* Mapping of Functional Data */

2009-12-11T21:09:47Z

Mapping of Functional Data

← Older revision		Revision as of 21:09, 11 December 2009
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	== Mapping of Functional Data ==		== Mapping of Functional Data ==

	Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface (Wandell, Chial, & Backus, 2000; [http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial]). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.		Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface (Wandell, Chial, & Backus, 2000; [http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial])). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.

imported>Psych 204: /* Conclusions */

2009-12-11T21:09:05Z

Conclusions

← Older revision		Revision as of 21:09, 11 December 2009
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	Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).		Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).

	Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and less jagged segmentation of white matter. Second, the FSL auto-segmenter would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards the use of a combination-approach to segmenting: ~~an approach that may~~ balance time~~-cost~~ and accuracy ~~would~~ be to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.		Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and less jagged segmentation of white matter. Second, the FSL auto-segmenter would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards the use of a combination-approach to segmenting: in order to balance demands on time and accuracy, it may be best to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.

	= Citations =		= Citations =

imported>Psych 204: /* Conclusions */

2009-12-11T21:07:49Z

Conclusions

← Older revision		Revision as of 21:07, 11 December 2009
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	Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).		Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).

	Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and less jagged segmentation of white matter. Second, the FSL ~~autosegmenter~~ would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards a combination-approach to segmenting: a approach that may balance time-cost and accuracy would be to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.		Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and less jagged segmentation of white matter. Second, the FSL auto-segmenter would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards the use of a combination-approach to segmenting: an approach that may balance time-cost and accuracy would be to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.

	= Citations =		= Citations =

imported>Psych 204: /* Conclusions */

2009-12-11T21:06:10Z

Conclusions

← Older revision		Revision as of 21:06, 11 December 2009
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	Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).		Regarding the secondary goal of conducting a meaningful comparison of the segmentations, meshes, and functional mappings, this project offers some initial insights but requires further work. The results described above would be greatly served by some manner of statistical analysis in order to investigate the relative significance of any differences between the alternate meshes and mappings produced by the three different segmentation performances. Additionally, this project employed ROI labels created for a specific mesh; as such, it would be useful to employ the proper ROIs for each functional mesh created (as noted during the discussion session for this project's in-class presentation).

	Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and ~~more~~ less jagged segmentation of white matter. Second, the FSL autosegmenter would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards a combination-approach to segmenting: a approach that may balance time-cost and accuracy would be to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.		Nonetheless, regardless of the above mentioned limitations, some general conclusions can be made regarding the different segmentation performances and their resultant effects on modeling brain structure and activity. First, it is evident that experience pays off for human segmenters - due diligence and practice in the exercise of ITK segmentation would appear to result in significantly fewer handles and less jagged segmentation of white matter. Second, the FSL autosegmenter would appear to be a powerful and useful tool, but it nonetheless makes some mistakes that may need to be corrected for. Therefore, in closing, if nothing else, this project would point towards a combination-approach to segmenting: a approach that may balance time-cost and accuracy would be to employ an auto-segementing algorithm and then apply an experienced human touch in order to correct for specific deficiencies.

	= Citations =		= Citations =

imported>Psych 204 at 09:59, 11 December 2009

2009-12-11T09:59:33Z

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	mrmViewer - http://white.stanford.edu/newlm/index.php/MrmViewer		mrmViewer - http://white.stanford.edu/newlm/index.php/MrmViewer

	~~= Appendix I - Code and Data =~~

	~~==Code==~~		= Appendix I =
	~~[[File:CodeFile.zip]]~~		A very sincere thanks to Jon Winawer for extended consultation and extensive patience in completion of this project.

	~~==Data==~~
	~~[[File:DataFile.zip \| zip file with my data]]~~

	= Appendix ~~II - Work partition (if a group project)~~ =
	~~Brian~~ and ~~Bob gave the lectures. Jon mucked around on the wiki~~.

imported>Psych 204: /* References */

2009-12-11T09:56:42Z

References

← Older revision		Revision as of 09:56, 11 December 2009
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	Fischl, B., Liu, A., & Dale, A.M. (2001). Autmated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex. ''IEEE Transactions on Medical Imaging, 20''(1), 70-80.		Fischl, B., Liu, A., & Dale, A.M. (2001). Autmated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex. ''IEEE Transactions on Medical Imaging, 20''(1), 70-80.

	Wandell, B.A.~~m Dumoulin~~, S.O., & ~~Brewer~~, A.A. (~~2007~~). ~~Visual Field Maps in Human Cortex~~. ''~~Neuron~~, 56'', ~~366~~-~~383~~.		Wandell, B.A., Chial, S., & Backus, B.T. (2000). Visualization and Measurement of the Cortical Surface. ''Journal of Cognitive Neuroscience, 12''(5), 739-752.

			Wandell, B.A., Dumoulin, S.O., & Brewer, A.A. (2007). Visual Field Maps in Human Cortex. ''Neuron, 56'', 366-383.

	== Software ==		== Software ==

imported>Psych 204: /* Mapping of Functional Data */

2009-12-11T09:54:56Z

Mapping of Functional Data

← Older revision		Revision as of 09:54, 11 December 2009
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	== Mapping of Functional Data ==		== Mapping of Functional Data ==

	Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface (Wandell, ~~Chialm~~ & Backus, 2000; [http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial]). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.		Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface (Wandell, Chial, & Backus, 2000; [http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial]). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.

imported>Psych 204: /* Mapping of Functional Data */

2009-12-11T09:54:25Z

Mapping of Functional Data

← Older revision		Revision as of 09:54, 11 December 2009
Line 56:		Line 56:
	== Mapping of Functional Data ==		== Mapping of Functional Data ==

	Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface ([http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial]). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.		Again, the true purpose of creating the mrVista meshes was to take advantage of their allowance of the mapping of functional data. The three respective meshes were all inflated (inflation of the meshes allows activity along buried sulci to be visible from the surface (Wandell, Chialm & Backus, 2000; [http://white.stanford.edu/~sayres/psy204/mrVistaTutorial.htm mrVista Tutorial]). After the meshes were inflated, the functional data was applied to the newly rendered surface. ROI labels were also applied to the meshes so as to provide reference in a visual comparison.