- Видео 7
- Просмотров 18 759
LESYMAP
Добавлен 2 июн 2017
Lesion to Symptom Mapping (R package)
Comparing SyNCC and SyN registration results
Typically SyNCC is slower but provides slightly more accurate results than SyN. The first uses cross-correlation similarity metric, while the second uses mutual information similarity metric. The example in the video is a brain with stroke registered with a cost masking function to remove the lesion area from computations.
Просмотров: 213
Видео
Registering stroke lesions to template space using LESYMAP
Просмотров 5416 лет назад
Description of why and how to register lesions in template space.
Fast manual lesion segmentation in ITKsnap
Просмотров 16 тыс.7 лет назад
When finished, save the newly drawn image with "Segmentation" - "Save Segmentation Image". This is a video-tutorial showing the basics of drawing brain lesions in ITKsnap. The interpolate tool allows the user to draw the lesion every few slices, and then interpolate the drawings to create a full lesion. Obviously, this method serves well if the lesion forms just one 3D shape. If the lesion for ...
Example SCCAN analysis in LESYMAP
Просмотров 3547 лет назад
Using the example provided with LESYMAP. It relies on a fixed sparseness value and takes ~8 minutes (runtime depends on computer). Sparse Canonical Correlation Analysis (SCCAN) is a multivariate method which provides much more circumscribed findings. In this example the simulated region is area 101 from parcellation template provided with LESYMAP. Note, this is just an example. SCCAN should nor...
Testing LESYMAP installation with provided example
Просмотров 4777 лет назад
Installating LESYMAP with devtools
Просмотров 5397 лет назад
OS information: Distributor ID: Ubuntu Description: Ubuntu 16.04.2 LTS Release: 16.04 Codename: xenial
LESYMAP Live Stream
7 лет назад
Comparing SCCAN with Brunner-Munzel
Просмотров 2247 лет назад
Lesion to symptom mapping with a multivariate (SCCAN) and univariate (BM) analysis. 93 brain areas simulated. Semi-transparent black indicated the true location of the simulated area. Subjects: 131 BM threshold: 0.05 SCCAN: sparseness: 0.45 iterations: 30 smoothing: 0.4 cthresh: 150 robust: true
Bro is labelling Sylvian fissure and calling it a lesion xd
Hello. I am trying to record lesions with lesymap but I am getting the following error: Error in checkMask(subImg, subLesion) : Mismatch directions of mask and 4D input. I don't know what can be wrong, this is de script: library(LESYMAP) anatomical = '/Users/scutiller/Desktop/lsm/segmentados_manual/subj002/subj002_DWI.nii.gz' lesion = '/Users/scutiller/Desktop/lsm/segmentados_manual/subj002/segmento.nii.gz' lesyreg = registerLesionToTemplate(subImg = anatomical, subLesion = lesion, outprefix = '/Users/scutiller/Desktop/lsm/segmentados_manual/subj002/to_template/') Thanks
Please open an issue in GitHub for better follow up. From the error, looks like you are trying to register a DWI image 4D, which that function cannot do. It can only register a T1 weighted to a template. Talk more on GitHub.
Hello, can you please tell me what is your professional occupation or what academic degree you have? I'm a biomedical engineer and I want to work with medical segmentation of the brain. Any tips on how to enter in the field? Thanks!
I am not a biomedical engineer but have a phd in neuroscience. I cannot give you a sound advice. It will likely depend on your geographical area, whether there is a university nearby, whether there is a group that does segmentations, etc. I can tell you that these things are not difficult after all. It is all a matter of numbers, all images are numbers, whether you take the picture with a phone or an mri machine.
@@lesymap1691 Thank you!
Hi! Thank you for such a great video. This is really helpful. I had a question after we are done with the segmentation how do we mark it as done.
You just save the segmentation file with "Segmentation" -> "Save". There is no other marking as done, the segmentation lives in the memory of the computer/itksnap until you save it out as a file.
Thank you for this very helpful video. I am wondering if I can know more about the reference of the paper (cranium 2013?) you mentioned in the video which provides the rule to identify lesions.
Should be: Crinion, J., Holland, A.L., Copland, D.A., Thompson, C.K., Hillis, A.E. (2013). Neuroimaging in aphasia treatment research: quantifying brain lesions after stroke.. NEUROIMAGE, 73 208-214. doi:10.1016/j.neuroimage.2012.07.044
Thank you very much sir for your valuable guidance. How to convert segmentation image into tiff format?
Not sure, but there is a function in the menu somewhere to output multiple slides as common bitmaps. Don't know if tiff is a format you can choose there.
Thank you, this video helped me to sort out several issues.
Thanks for this useful video. I have a question. How can we measure the volume of the area to the segment? After loading three dimensions, I find a result from the segmentation and volume option, but it is not very accurate.
If you go to Segmentation -> Volumes and Statistics in ITKsnap, you should find the volume you need. The volume is the product of voxel size by the number of voxels. You can also calculate it in other programs outside ITKsnap.
When we do, we find the value containing "e". I was stuck here. I found the solution. We multiply by 10 as the number of digits after "e". The next value we found is the volume in mm3.
sir can you tell me which Statistics method is used in lesymap and please add you email
can you tell which version of r and r studio you are using because i tried 3 r with different r studio and every time i get the same error that lesymap is not avaliable for this r version.
Should work with R v3.5 at least. But none of the key neuroimaging packages are in cran, so you cannot install it with the usual install.packages(). Please check the github page for how to install.
@@lesymap1691 i want to use lesymap in rstudio windows version ..
@@aanchalchoudhary9934 Can't use lesymap in directly on Windows. The only way is to use Windows Linux Subsystem. Docker, or some virtual machine with linux in it.
@@lesymap1691 thank you so much...
I get the following error, how can i sort it? ld: warning: directory not found for option '-L/usr/local/gfortran/lib/gcc/x86_64-apple-darwin15/6.1.0' ld: warning: directory not found for option '-L/usr/local/gfortran/lib' ld: library not found for -lgfortran clang: error: linker command failed with exit code 1 (use -v to see invocation) make: *** [LESYMAP.so] Error 1 ERROR: compilation failed for package ‘LESYMAP’ * removing ‘/Library/Frameworks/R.framework/Versions/3.5/Resources/library/LESYMAP’ Errore: Failed to install 'LESYMAP' from GitHub: (contertito da avviso) installation of package ‘/var/folders/rd/dk6dj8t971j0xl_whkrbdpyr0000gn/T//RtmpKDViVZ/file13a556d2d1942/LESYMAP_0.0.0.9221.tar.gz’ had non-zero exit status
Sorry Gianluca, not sure what is going on with this error. Please open an issue on github if the issue persists.
I can't install lesymap in R Studio. Is it because of windows OS?
Yes, you need to use Linux, Mac, or Windows Linux Subsystem. There is even a very easy option to get and run the prepared Docker container right away github.com/dorianps/docker
@@lesymap1691 thanks. I will try this
Suppose you have 53 MRI's with lesions, how do you assign all of them into the same template space? This video provides an example for putting only one lesion into template space. Can someone help me with this?
You can run registerLesionToTemplate to every subject. The results final result for everyone will be a lesion in the same space. There is not single line call to register all at once, you need to do them one by one in a for loop. Please post future questions on github: github.com/dorianps/LESYMAP/issues
Appreciate your videos!
very helpful video. I wonder if I could get the link to download paper mentioned in this video (5:02) about rules of drawing lesion. anyone?
Link: www.ncbi.nlm.nih.gov/pmc/articles/PMC3534842/ We are also working on a book that will contain segmentation rules, but it will not be out before next year.
Thanks a lot!
Helpful! Thank you!