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.

Functional Mapping

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

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 ("FSL Misses" as seen in Figure XXXX 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 ("FSL 'False Alarms'" as seen in Figure XXXX 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), the algorithm also would appear to in some regions segment white matter much closer to the brain surface than does the human. 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.

Below is an example of a retinotopic map. Or, to be precise, below will be an example of a retinotopic map once the image is uploaded. To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure.

Below is another example of a reinotopic map in a different subject.
Figure 2

Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.

Figure 3

MNI space

MNI is an abbreviation for Montreal Neurological Institute.

Results - What you found

Retinotopic models in native space

Some text. Some analysis. Some figures.

Retinotopic models in individual subjects transformed into MNI space

Some text. Some analysis. Some figures.

Retinotopic models in group-averaged data on the MNI template brain

Some text. Some analysis. Some figures. Maybe some equations.

Equations

If you want to use equations, you can use the same formats that are use on wikipedia.
See wikimedia help on formulas for help.
This example of equation use is copied and pasted from wikipedia's article on the DFT.

The sequence of N complex numbers x₀, ..., x_N−1 is transformed into the sequence of N complex numbers X₀, ..., X_N−1 by the DFT according to the formula:

$X_k=\sum _{n=0}^{N-1}{x}_n{e}^{-\frac{2\pi i}{N}kn}k=0,\dots ,N-1$

where i is the imaginary unit and $e^{\frac{2\pi i}{N}}$ is a primitive N'th root of unity. (This expression can also be written in terms of a DFT matrix; when scaled appropriately it becomes a unitary matrix and the X_k can thus be viewed as coefficients of x in an orthonormal basis.)

The transform is sometimes denoted by the symbol $ℱ$, as in $𝐗=ℱ\left\{𝐱\right\}$ or $ℱ\left(𝐱\right)$ or $ℱ𝐱$.

The inverse discrete Fourier transform (IDFT) is given by

$x_n=\frac{1}{N}\sum _{k=0}^{N-1}{X}_k{e}^{\frac{2\pi i}{N}kn}n=0,\dots ,N-1.$

Retinotopic models in group-averaged data projected back into native space

Some text. Some analysis. Some figures.

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.