Hello,
mri_watershed was specifically designed for the skull-stripping of human brain data. Not surprisingly, it might fail on hi-res monkey data.
However, here is a short watershed trouble shooting guide that might help you find a valid set of options to successfully skull-strip your images.
Good Luck,
Best,
Florent ----------------------------------------------------------- WATERSHED TROUBLESHOUTING GUIDE:
I am putting below some suggestions about how to find the correct set of parameters without spending too much time on it. I think that there are sufficient options to get a correct skull-stripping on almost every sca ns.
Before getting into the details, you have to know that mri_watershed is a pipelined process, composed of two main steps. The first one ends with the first template smoothing and is composed of 3 substeps (watershed segmentation, post-processing and first template smoothing). The second step corresponds to a fine template deformation.
HOW TO PROCEED TO OBTAIN A VALID SKULL-STRIPPING (when it fails with the default options):
* If the skull-stripping fails, my first suggestion is to try to understand why it is failling. Rerun the mri_watershed program with the -surf_debug option, which writes out two surfaces into the output volume (first template smoothing and fine template deformation). V * Looking at the output, there are two possibilities: either the first step fails (this corresponds to the first template smoothing, which is the exterior surface into the output volume), or the second step fails (the fine template deformation = the interior surface). Let's analyze the two cases separately:
1) Failing of the first segmentation step. This first step is the result of 3 pipelined sub-steps: watershed segmentation, post-processing and first template smoothing. In order to fix the problem, there are a few choices about how to proceed. 1-1) "I-am-feeling-lucky" choice: you might want to go down this path, if the skull-stripping removed a few brain regions. The best option is probably to try to add seed points in the excluded brain regions (with the -s option), even though you could increase the preflooding height (-h option). In my experience, there is no point in trying thousand of values for the preflooding height. A couple of tries are sufficient. If it does not work for these, then, try with more seed points... 1-2) "what-is-failling" approach: since the program fails during the first step, it could come either from the watershed segmentation, or the post-processing step, or the first template smoothing. One way to find out is to first run the program with the option -wat and -n: this will stop the program right after the watershed segmentation before the post-processing. If the segmentation is already failing, try adding seed points with the option -s or play with the preflooding height -h. In my experience, the best way to quickly find the right set of parameters is to use seed points (potentially lots of them). In order to find the right set of parameters, just run the program with the options "-wat -n". This will be much faster. nce the right set of parameters have been found, run the program with the "-n" option, as the postprocessing step should be useless... If the program fails after the watershed processing, then this means that the post-processing is probably producing the errors. This is often resulting in too much tissue being included. Simply run the program with the "-n" option.
2) In case of failure during the second step (fine surface deformation), you have several option (most failures come from the first step). 2-1) if you think that the first step produced a correct segmentation, you can directly stop the segmentation at this point with the -wat+temp option. 2-2) you think that the first step produced a good intialization for the brain surface and that it needs to be refined a little: use the -first_temp option. 2-3) try the -atlas option that will incorporate shape information into the deformation process and should give some correct results!
IN SUMMARY:
1) Try to understand what's happening with the option "-surf_debug"
2) Try the option "-atlas" if the program advises you to do so, or try it too if you have time for it (~10 minutes)! "I usually skip this step".
3) Depending on what you see with the surf_debug option, you may try to correct the watershed segmentation or the fine segmentation.
* To correct the watershed segmentation, you can:
- play with the preflooding height (use the -wat option to visualize the result quickly, without having to run all the program).
- modify or suppress the post-watershed analysis mode (-n to suppress the post_watershed analysis, -t to change the threshold used in the merging of the basins).
- use some (lots of) seed points... THIS IS WHAT WORKS BEST AND QUICKLY.
IMPORTANT: In order to experiment with the options -n, -h, -s, don't forget to run the program with the option -wat. (no need to run all the program).
SOME OTHER MODE ADVANCED TIPS:
o You can set the global parameters manually, if you think that the ones found during the process are incorrect: this is the -man option. You would have to precise the csf max, the transition intensity, and the gray matter intensity.
o The difference between -first_temp and -wat+temp is that the first option will use the first surface to compute the local intensity values at the border of the brain and then try to shrink it a bit. The second option -wat_temp will only use the first surface and immediately segment the brain out.
PARTIAL LIST OF OPTIONS (command line: "mri_watershed -option")
Option -wat : option that stops the algorithm after the initial watershed segmentation (the post-analyze segmentation is included).
Option -h hpf , with hpf an integer: modification of the preflooding height used in the watershed segmentation. A typical value for hpf is 25 (corresponding to 25% of the maximal intensity); lower values tend to decrease the size of the main basin (which could be infirmed by the post-analyze process) and higher values increase its size. However, for any given image, the preflooding height hpf can be varied over a certain range without significantly changing the output.
Option -n : the post-analyze is not used with this option.
Option -t th, with th an integer: that option specifies the threshold used in the post-watershed analysis. The default value is 100 (100%, meaning that the threshold is exactly the cubic root of the size of the ambiguous basin). A value of 0 will result in a post-watershed analysis that will merge any ambiguous basin with the main one.
Option -wat+temp : with this option, only the watershed segmentation and the first template deformation is used. This option could be typically used when the image contains a lot of artifacts, resulting in a fine surface segmentation that shaves some part of the cortex; in that extreme case, the watershed segmentation may still lead to successful results and the first template deformation to a valid brain segmentation.
Options -less or -more: option designed to increase or decrease the transition threshold used in the fine surface. The result is respectively a shrunk or expanded surface compared to the typical surface.
Option -surf_debug: this option will write out, into the output volume, the localization of the different surfaces used during the algorithm; it will enable the user to visualize the results of the different template deformations, and eventually to detect, understand and correct some errors.
Option -s int_i int_j int_k : this option allows the user to add some new seed points for the watershed process. This option is very useful when the cerebellum is removed by the watershed segmentation, since a seed point inside the removed cerebellum may be added, leading to the merging of the corresponding basin with the main one. However, the atlas-based validation-correction step should be sufficient to correct the segmentation.
Option -man int_CSF int_transition int_GM: this option allows the user to manually set the parameters used in the fine surface deformation.
Option -surf surface_name: this option is used to generate tessellated surfaces of the brain, inner and outer skull, and outer scalp from the T1-weighted image. The brain surface is the one produced by the fine surface deformatio n described. The outer scalp surface is generated by another surface deformation. However, the MRI-based force is quite simple, since the scalp appears bright on T1-weighted images. The deformation is initialized with a radius set to include the whole head, and a series of iterative deformation matches the surface onto the head scalp-skin. The outer and inner skull surfaces are much harder to find, since the skull and the cerebrospinal fluid look the same in these type of images (dark intensity). Besides, the skull may contain some fat tissue that makes the border between the scalp and the outer skull difficult to localize. For these reasons, the two surfaces, outer skull and inner skull, are respectively interpolated from the outer scalp and brain surface. They are a smoothed version of the corresponding surface that has been respectively shrunk or expanded a few millimeters inwards or outwards (3 mm).
Option -brainsurf surface_name: that option writes out the final tessellated brain surface.
Option -shk_br_surf hshk surface_name: this option writes out the final tessellated brain surface that has been shrunk hshk mm inwards.
Option -LABEL: this option enables the user to obtain a labeled volume into 5 classes: scalp, skull, csf, gray and white matter. First the whole algorithm is run and the surfaces corresponding to the brain, outer scalp, outer skull, inner skull are estimated. Based on the localization of these surfaces, we label the volume into brain, scalp, skull, and csf ; then, the brain segment is divided into gray an white matter based on the intensity statistics calculated during the template deformation process.
Option -atlas: with that option, the program uses an atlas, compiled from successfully segmented brains, in order to detect the rightness of the surface, and to eventually correct it. That should solve some of the problems, especially those when the cerebellum is removed (or part of it!).
Option -first_temp: with that option, the final deformation uses the first template deformation as a starting point.
------------------------------------- Florent Segonne PhD Candidate Stata Center 32-D430 CSAIL MIT 1 617 253 2986 http://people.csail.mit.edu/~fsegonne -------------------------------------
On Thu, 5 May 2005, Bruce Fischl wrote:
Hi Flo,
could you send your watershed troubleshooting guide to the BWH folks (ccd) so they can include it in the manual?
thanks, Bruce