Segmentation FAQ

Frequently Asked Questions - Segmentation

1. What is segmentation?

Segmentation is the process by which you separate your brain pictures into different tissue types. You give the segmentation program a brain image, and it classifies every voxel by tissue type - grey matter, white matter, CSF, skull, etc. Some segmentation algorithms operate on a probabilistic basis rather than a "hard" classification (so one voxel might by 60% likely to be grey, 10% likely to be white, etc.). Some segmentation algorithms go further than tissue type, and classify individual anatomical regions as well (see SegmentationPapers). Segmentation algorithms often give back output images, consisting of all the grey voxels in the brain, for example.

A subset of segmentation algorithms focus only on the problem of separating brain from skull tissue; these are often called "skull-stripping" or "brain-extraction" algorithms. The problem of classifying brain from skull is slightly easier than classifying different brain tissue types, but many of the same problems are faced, so we lump them in together with general segmentation algorithms.

2. Why should you segment?

Lotta reasons. Might be you're interested in the details of the segmentation - how much gray matter is in a particular region, how much white matter, etc. A lot of those analyses fall under the label of voxel-based morphometry (VBM), discussed below in the Ashburner & Friston paper. Alternatively, you might be interested in masking your analysis with one of the segments and only examining activated voxels that are in gray matter in a particular region. You might want to segment only to increase the accuracy of another preprocessing step - you might care that your normalization, say, is especially good in gray matter while you don't care as much about its accuracy in white matter. Simply extracting the brain has even more utility; some analysis programs or preprocessing steps require you to strip skull tissue off the brain before using them. You might simply want to create an analysis mask of all the brain voxels and ignore the other ones.

All of those issues would require you to identify which voxels of your image (almost always anatomical) are gray matter, which are white, and which are CSF or skull or other stuff (or at least which are brain and which are not). You can do this by hand, but it's an arduous and hugely time-consuming process, infeasible for large groups of subjects. Several automated methods are available, though, to do it. Generally, the algorithms take some input image and produce labels for every voxel, assigning them to one of the categories above, or sometimes anatomical labels as well (see below). Alternativey, some algorithms exist that do a "soft" classification and assign each voxel a certain probability of being a particular tissue type. Which you use will depend on exactly what your goals for segmentation are.

Finally, segmentation algorithms are increasingly being used not only to separate tissue types but to automate the production of individualized anatomical ROIs. Would you like to hand-draw your caudate or thalamus, say, but figure it'll take too long or be too hard? Automated segmentation algorithms could be used to simplify the process.

3. What are the problems I might face with segmentation?

Segmentation algorithms face two big issues: intensity overlap and partial voluming. Intensity overlap refers to the fact that the intensity distributions for different tissue types aren't completely separate - they have significant overlap, such that a bright voxel might be a particularly bright gray matter voxel or, just as easily, a particularly bright white matter voxel. Because all segmentation algorithms have to work with is the intensity value at each voxel (and those around it), this poses a problem for hard classifications. As well, inhomogeneities in the magnetic field, susceptibility-induced magnetic changes or head motion during acquisition can all produce gradual shadings of light or dark in images that can make the different tissue types even harder to distinguish - a particular brightness level might be gray matter at the front of the head, but white matter at the back of the head. One way to address these problems is to take spatial location into account; at the simplest level, voxels can always be assigned a high probability of being the same tissue type as the voxels around them (spatial coherence), or one can use a more sophisticated method like incorporating a full prior probability atlas (see Fischl et. al and Marroquin et. al, below).

Partial voluming refers to the fact that even high-res MRI has a limited spatial resolution, and a given voxel might include signal from several different tissue types to varying degree. This is particularly important along tissue-type borders, where if an algorithm is biased towards one tissue type or another, estimates of one tissue type's volume within an area can be significantly inflated or deflated from reality. One way to address this problem is with "soft" classification - instead of semi-arbitarily assigning voxels to definite tissue types, one can assign voxels probabilities of being in a tissue type, and take that confidence level into account when deciding tissue volume, etc.

4. How are coregistration and segmentation related?

Fischl et. al (SegmentationPapers) make the point that the two processes operate on different sides of the same coin - each one can solve the other. With a perfect coregistration algorithm, you could be maximally confident that you could line up a huge number of brains and create a perfect probability atlas - allowing you the best possible prior probabilities with which to do your segmentation. In order to do a good segmentation, then, you need a good coregistration. But if you had a perfect segmentation, you could vastly improve your coregistration algorithm, because you could coregister each tissue type separately and greatly improve the sharpness of the edges of your image, which increases mutual information.

Fortunately, MI thus far appears to do a pretty good job with coregistration even in unsegmented images, breaking us out of a chicken-and-egg loop. But future research on each of these processes will probably include, to a greater and greater extent, the other process as well.

Check out CoregistrationFaq for more info on coregistration...


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