2009 Jim Cummings

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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. This process 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. 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 autosegmenting program.

Methods

Image Acquisition

All segmentations conducted were based on a single brain scan stored on file in the VISTA Lab. This file was provided by Jonathan Winawer

Segmentation

The amateur segmentation of the greyscale brain image was conducted using ITK-Snap and following directions supplied within VISTA Lab manual. The experienced segmentation of the same greyscale image had been previously completed by a member of the VISTA Lab and was provided by Jonathan Winawer. Finally, the algorithmic segmentation was conducted using the FSL automated preprocessing tool as implemented through VISTASOFT and following the directions supplied within the VISTA Lab manual.

Additionally, the segmentations were then transformed into three-dimensional meshes using ITK-Snap's built-in mesh constructor, allowing for initial comparison between performances.

Mapping of Functional Data

The three segmentations were also then transformed into three separate three-dimensional meshes using mrVista. Functional data maps were then projected onto these meshes in order to compare how structural differences may result in distinctive mappings. The functional data was of vision-related activity during a word rotation task. The data was from the same brain as the greyscale image employed for segmentation. The data file was provided by Jonathan Winawer.

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.

Segmentation and ITK-Snap Meshes

Figure 1. Mesh of experienced segmentation

Figure 2. Mesh of algorithm segmentation

Initial comparison of the amateur and experienced segmentation performances revealed a large difference in the number of handles produced during segmentation. Handles are essentially cavities within areas that have been segmented as white matter. Fixing them depends on their nature of origin: handles produced merely by failing to segment a patch within a region should be filled in; handles that result from falsely segmenting too far outwards (towards gray matter) should be resolved by removing the extraneous segmentation that produced the cavity. The large number of handles produced by the amateur segmentation would suggest a less cautious or diligent effort during the fine segmentation stage. This finding would indicate that a mere visual comparison of the the resulting ITK meshes for the amateur and experienced segmentations was deceiving; indeed, these meshes were largely similar, though the some handles (of the second type mentioned above) were apparent.

The algorithm segmentation, too, produced a sizable number of handles. However, of real comparative interest is the visual distinction between the meshes produced by algorithm and the experienced segmenter. Indeed, a three-dimensional mesh of solely the regions segmented by the experienced segmenter but not the algorithm (Algorithm "Misses" as seen in Figure 3 below) reveals some differences quite obvious to the naked eye. Of particular interest is the finding that the algorithm failed to segmented a sizable (again, statistical analysis absent from this report) amount of the ventral right hemisphere (indicated in blue). Similarly, a mesh of the material segmented by the algorithm but not the experienced segmenter (Algoritm "False Alarms" as seen in Figure 4 below) indicates some interesting differences. In addition to the algorithm segmenting the cerebellum (which is not of interest and thus not segmented by an experienced human segmenter), in some regions it would appear to also segment white matter much closer to the brain surface than does the human. This is the case for the anterior brain. Whereas a human segmenter has the contextual knowledge to not segment white matter to this proximity to the brain surface and CSF (as he would factor in a layer of gray matter), the algoritm would appear to fail in this regard.

Figure 3. FSL Algorithm "Misses"

Figure 4. FSL Algorithm "False Alarms"

mrVista Meshes

As mentioned above, new meshes were then created using mrVista (which would allow for the eventual projection of functional maps). In light of the functional data file available, only right hemisphere meshes were created for each of the three segmentations. Figure 5 below offers a comparison of the occipital areas of the right hemisphere meshes created from the experienced and amateur segmentations. Of note are distinctions similar to those observed when comparing the ITK meshes: the amateur mesh tended to have more visible handles, more jagged fold curvature, and differences in general fold shape and structure.

A visual comparison of mrVista meshes created from the experienced and algorithm segmentations yields similar though somewhat less pronounced results. The algorithm mesh holds some visible handles, jagged edges, and some differences in the general fold structure. However, the algorithm mesh's distinctions from the experienced mesh are perhaps less severe that those between the experienced and the amateur, in that 1) much of the jaggedness is found within the cerebellum (and is thus not of concern), and 2) by and large, the relative differences in general fold structure are less dramatic.

Figure 5. Comparing Experienced and Amateur mrVista meshes

Figure 6. Comparing Experienced and Algorithm mrVista meshes

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 (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.

As mentioned above, the functional data used here was an activation set pertaining to a word rotation task. During such a task, a word rotates on the subject's screen; different orientations of the word are thought to correlated with different regions of activation. The activation for each orientation is depicted within the functional map with a given color, as seen in the color scheme presented in Figure 7.

Figure 7. Color key for rotation perception activation

Activity mapped onto the meshes should be largely structured by color demarcation: regions for a given word orientation perception should be represented by relatively solid strands of a given color. In comparing the functional meshes created from the experienced and amateur segmentations (Figure 8.), we see that this is largely the case with the experienced mesh; however, when the same activity data gets mapped onto the amateur mesh, it becomes distorted with splotches of color pervading regions of alternate types of activity. Additionally, when the activity is mapped onto the experienced mesh, it would appear to most closely fit the regional demarcations depicted by the ROI labels.

When visually comparing the functional meshes produced by the experienced and algorithm segmentations (Figure 9.), we see that the functional data mapping for the algorithm is far more similar to the end result produced by the amateur segmentation than the end result produced by the experienced segmentation. Not only does the algorithm mesh contain specks of activity color scattered throughout alternate color strands, but - also like the function mapping for the amateur mesh - the strands themselves fit the ROI labels less accurately relative to the those projected onto the experienced mesh.

Figure 8. Comparing Experienced and Amateur mrVista functional meshes

Figure 9. Comparing Experienced and Algorithm mrVista functional meshes

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Conclusions

Here is where you say what your results mean.

References - Resources and related work

References

Software

Appendix I - Code and Data

Code

File:CodeFile.zip

Data

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.